Decreased numerical density of kainate receptor-positive neurons in the orbitofrontal cortex of chronic schizophrenics

We utilised postmortem brain tissue to quantify sections of left and right orbitofrontal cortex (area 11) from nine schizophrenic and eight control patients from the Charing Cross Prospective Schizophrenia Study immunostained for the presence of the kainate receptor (GluR5/6/7). The numerical density of neurons immunopositive for kainate receptor was measured. Other sections from the same blocks were stained with cresyl violet to determine the total neuronal numerical density. All measurements were made blind: diagnoses were only revealed by a third party after measurements were completed. There was a significant reduction (21%) in numerical density of kainate receptor-positive neurons in both cortices in the schizophrenic group (488cells/mm2) compared to that in the control group (618cells/mm2) (P=0.033). Nissl-stained tissue showed no significant difference in total neuronal numerical density between control and schizophrenic groups. These observations suggest that there are actually fewer kainate receptor-positive neurons in schizophrenic orbitofrontal cortex. There was no correlation of reduced kainate receptor-positive cell number with age at death, postmortem interval, or other possibly confounding neuropathology. Our results support the concept of there being reduced glutamatergic activity in frontal cortex in schizophreni

Semi-autonomous Intersection Collision Avoidance through Job-shop Scheduling

In this paper, we design a supervisor to prevent vehicle collisions at intersections. An intersection is modeled as an area containing multiple conflict points where vehicle paths cross in the future. At every time step, the supervisor determines whether there will be more than one vehicle in the vicinity of a conflict point at the same time. If there is, then an impending collision is detected, and the supervisor overrides the drivers to avoid collision. A major challenge in the design of a supervisor as opposed to an autonomous vehicle controller is to verify whether future collisions will occur based on the current drivers choices. This verification problem is particularly hard due to the large number of vehicles often involved in intersection collision, to the multitude of conflict points, and to the vehicles dynamics. In order to solve the verification problem, we translate the problem to a job-shop scheduling problem that yields equivalent answers. The job-shop scheduling problem can, in turn, be transformed into a mixed-integer linear program when the vehicle dynamics are first-order dynamics, and can thus be solved by using a commercial solver.Comment: Submitted to Hybrid Systems: Computation and Control (HSCC) 201

Identification of network modules by optimization of ratio association

We introduce a novel method for identifying the modular structures of a network based on the maximization of an objective function: the ratio association. This cost function arises when the communities detection problem is described in the probabilistic autoencoder frame. An analogy with kernel k-means methods allows to develop an efficient optimization algorithm, based on the deterministic annealing scheme. The performance of the proposed method is shown on a real data set and on simulated networks

On the Complexity of Searching in Trees: Average-case Minimization

We focus on the average-case analysis: A function w : V -> Z+ is given which defines the likelihood for a node to be the one marked, and we want the strategy that minimizes the expected number of queries. Prior to this paper, very little was known about this natural question and the complexity of the problem had remained so far an open question. We close this question and prove that the above tree search problem is NP-complete even for the class of trees with diameter at most 4. This results in a complete characterization of the complexity of the problem with respect to the diameter size. In fact, for diameter not larger than 3 the problem can be shown to be polynomially solvable using a dynamic programming approach. In addition we prove that the problem is NP-complete even for the class of trees of maximum degree at most 16. To the best of our knowledge, the only known result in this direction is that the tree search problem is solvable in O(|V| log|V|) time for trees with degree at most 2 (paths). We match the above complexity results with a tight algorithmic analysis. We first show that a natural greedy algorithm attains a 2-approximation. Furthermore, for the bounded degree instances, we show that any optimal strategy (i.e., one that minimizes the expected number of queries) performs at most O(\Delta(T) (log |V| + log w(T))) queries in the worst case, where w(T) is the sum of the likelihoods of the nodes of T and \Delta(T) is the maximum degree of T. We combine this result with a non-trivial exponential time algorithm to provide an FPTAS for trees with bounded degree

Evaluation of the bank stability and toe erosion model (BSTEM) for predicting lateral retreat on composite streambanks

Streambank erosion is known to be a major source of sediment in streams and rivers. The Bank Stability and Toe Erosion Model (BSTEM) was developed in order to predict streambank retreat due to both fluvial erosion and geotechnical failure. However, few, if any, model evaluations using long-term streambank retreat data have been performed. The objectives of this research were to (1) monitor long-term composite streambank retreat during a hydraulically active period on a rapidly migrating stream, (2) evaluate BSTEM’s ability to predict the measured streambank retreat, and (3) assess the importance of accurate geotechnical, fluvial erosion, and near-bank pore-water pressure properties. The Barren Fork Creek in northeastern Oklahoma laterally eroded 7.8 to 20.9 m along a 100-m length of stream between April and October 2009 based on regular bank location surveys. The most significant lateral retreat occurred in mid- to late-May and September due to a series of storm events, and not necessarily the most extreme events observed during the monitoring period. BSTEM (version 5.2) was not originally programmed to run multiple hydrographs iteratively, so a subroutine was written that automatically input the temporal sequence of stream stage and to lag the water table in the near-bank ground water depending on user settings. Eight BSTEM simulations of the Barren Fork Creek streambank were performed using combinations of the following input data: with and without a water table lag; default BSTEM geotechnical parameters (moderate silt loam) versus laboratory measured geotechnical parameters based on direct shear tests on saturated soil samples; and default BSTEM fluvial erosion parameters versus field measured fluvial erosion parameters from submerged jet tests. Using default BSTEM input values underestimated the actual erosion that occurred. Lagging the water table predicted more geotechnical failures resulting in greater streambank retreat. Using measured fluvial and geotechnical parameters and a water table lag also under predicted retreat (approximately 3.3 m), but did predict the appropriate timing of streambank collapses. The under prediction of retreat was hypothesized to be due to over predicting the critical shear stress of the non-cohesive gravel, under predicting the erodibility of the non-cohesive gravel, and/or under predicting the imposed shear stress acting on the streambank. Current research improving our understanding of shear stress distributions, streambank pore-water pressure dynamics, and methods for estimating excess shear stress parameters for noncohesive soils will be critical for improving BSTEM and other streambank stability models

Evaluation of the bank stability and toe erosion model (BSTEM) for predicting lateral retreat on composite streambanks

Streambank erosion is known to be a major source of sediment in streams and rivers. The Bank Stability and Toe Erosion Model (BSTEM) was developed in order to predict streambank retreat due to both fluvial erosion and geotechnical failure. However, few, if any, model evaluations using long-term streambank retreat data have been performed. The objectives of this research were to (1) monitor long-term composite streambank retreat during a hydraulically active period on a rapidly migrating stream, (2) evaluate BSTEM’s ability to predict the measured streambank retreat, and (3) assess the importance of accurate geotechnical, fluvial erosion, and near-bank pore-water pressure properties. The Barren Fork Creek in northeastern Oklahoma laterally eroded 7.8 to 20.9 m along a 100-m length of stream between April and October 2009 based on regular bank location surveys. The most significant lateral retreat occurred in mid- to late-May and September due to a series of storm events, and not necessarily the most extreme events observed during the monitoring period. BSTEM (version 5.2) was not originally programmed to run multiple hydrographs iteratively, so a subroutine was written that automatically input the temporal sequence of stream stage and to lag the water table in the near-bank ground water depending on user settings. Eight BSTEM simulations of the Barren Fork Creek streambank were performed using combinations of the following input data: with and without a water table lag; default BSTEM geotechnical parameters (moderate silt loam) versus laboratory measured geotechnical parameters based on direct shear tests on saturated soil samples; and default BSTEM fluvial erosion parameters versus field measured fluvial erosion parameters from submerged jet tests. Using default BSTEM input values underestimated the actual erosion that occurred. Lagging the water table predicted more geotechnical failures resulting in greater streambank retreat. Using measured fluvial and geotechnical parameters and a water table lag also under predicted retreat (approximately 3.3 m), but did predict the appropriate timing of streambank collapses. The under prediction of retreat was hypothesized to be due to over predicting the critical shear stress of the non-cohesive gravel, under predicting the erodibility of the non-cohesive gravel, and/or under predicting the imposed shear stress acting on the streambank. Current research improving our understanding of shear stress distributions, streambank pore-water pressure dynamics, and methods for estimating excess shear stress parameters for noncohesive soils will be critical for improving BSTEM and other streambank stability models

On the NP-Hardness of Approximating Ordering Constraint Satisfaction Problems

We show improved NP-hardness of approximating Ordering Constraint Satisfaction Problems (OCSPs). For the two most well-studied OCSPs, Maximum Acyclic Subgraph and Maximum Betweenness, we prove inapproximability of $14/15+\epsilon$ and $1/2+\epsilon$. An OCSP is said to be approximation resistant if it is hard to approximate better than taking a uniformly random ordering. We prove that the Maximum Non-Betweenness Problem is approximation resistant and that there are width-$m$ approximation-resistant OCSPs accepting only a fraction $1 / (m/2)!$ of assignments. These results provide the first examples of approximation-resistant OCSPs subject only to P $\neq$ \NP

Scheduling over Scenarios on Two Machines

We consider scheduling problems over scenarios where the goal is to find a single assignment of the jobs to the machines which performs well over all possible scenarios. Each scenario is a subset of jobs that must be executed in that scenario and all scenarios are given explicitly. The two objectives that we consider are minimizing the maximum makespan over all scenarios and minimizing the sum of the makespans of all scenarios. For both versions, we give several approximation algorithms and lower bounds on their approximability. With this research into optimization problems over scenarios, we have opened a new and rich field of interesting problems.Comment: To appear in COCOON 2014. The final publication is available at link.springer.co

Detection of Complex Networks Modularity by Dynamical Clustering

Based on cluster de-synchronization properties of phase oscillators, we introduce an efficient method for the detection and identification of modules in complex networks. The performance of the algorithm is tested on computer generated and real-world networks whose modular structure is already known or has been studied by means of other methods. The algorithm attains a high level of precision, especially when the modular units are very mixed and hardly detectable by the other methods, with a computational effort ${\cal O}(KN)$ on a generic graph with $N$ nodes and $K$ links.Comment: 5 pages, 2 figures. Version accepted for publication on PRE Rapid Communications: figures changed and text adde

Message passing for vertex covers

Constructing a minimal vertex cover of a graph can be seen as a prototype for a combinatorial optimization problem under hard constraints. In this paper, we develop and analyze message passing techniques, namely warning and survey propagation, which serve as efficient heuristic algorithms for solving these computational hard problems. We show also, how previously obtained results on the typical-case behavior of vertex covers of random graphs can be recovered starting from the message passing equations, and how they can be extended.Comment: 25 pages, 9 figures - version accepted for publication in PR