Autonomic load balancing of flow monitors

In monitoring flows at routers for flow analysis or deep packet inspection, the monitor calculates hash values from the flow ID of each packet arriving at the input port of the router. Therefore, the monitors must update the flow table at the transmission line rate, so high-speed and high-cost memory, such as SRAM, is used for the flow table. This requires the monitors to limit the monitoring target to just some of the flows. However, if the monitors randomly select the monitoring targets, multiple routers on the route will sometimes monitor the same flow, or no monitors will monitor a flow. To maximize the number of monitored flows in the entire network, the monitors must select the monitoring targets while maintaining a balanced load among them. We propose an autonomous load-balancing method where monitors exchange information on monitor load only with adjacent monitors. Numerical evaluations using the actual traffic matrix of Internet2 show that the proposed method improves the total monitored flow count by about 50% compared with that of independent sampling. Moreover, we evaluate the load-balancing effect on 36 backbone networks of commercial ISPs.     

Transflective IPS‐LCD with improved reflective contrast ratio

Abstract— In order to improve the reflective contrast ratio of transflective IPS‐LCDs, a novel pixel design for a normally white reflective IPS has been proposed. In this design, the large‐inter‐electrode‐spacing layout using a novel driving method and a double‐layered electrode have effectively reduced the light leakage. By applying these two technologies, a transflective IPS‐LCD has been successfully demonstrated with a high contrast ratio (15:1) in the reflective mode and a wide‐viewing‐angle characteristic in the transmissive mode.     

Ergonomics solution for crossing collisions based on field assessment of visual environment at urban intersections in Japan

This paper aims to assess quantitatively the actual visual environment of uncontrolled urban downtown intersections in Japan in relation to frequently occurring crossing collisions and to discuss the safety countermeasures for them. In Field Study 1 dealing with direct visibility, our ultra-wide-angle photograph analysis revealed that most of the right/left-ward visible range at 11 intersections were insufficient to check safety, and the quality of direct visibility was closely associated with causing crossing collisions. The countermeasures to reduce a blind area were determined to be a top priority. In Field Study 2 dealing with indirect visibility, more than half of the 25 traffic convex mirrors had marked shortcomings for preventive safety, and ergonomics guidelines ensuring indirect visibility were proposed for installing traffic convex mirrors. Low-cost/low-technology-oriented countermeasures are highly recommended to obtain clear/sufficient images of crucial information satisfying drivers' requirements on traffic convex mirrors in accordance with those ergonomics guidelines was highly recommended. Crossing collisions could be prevented by improvement of poor direct and indirect visibility.     

Studies of 2D Cryocrystals by STM Techniques

Scanning tunneling microscopy (STM) studies of two-dimensional (2D) cryocrystals ⁴ He, Kr, Xe) physisorbed on graphite surfaces are presented. Individual helium atoms, usually thought to be invisible with STM, were recently observed on graphite surfaces at a density corresponding to the commensurate solid. Here we show that a local elastic deformation seems to be the principal mechanism responsible to render the atoms visible. Recent tight-binding calculations of the local density of states (LDOS) of graphite which predict the appearance of an energy gap support this picture. I-z curve measurements for the case of ⁴ He show a sharp drop (increase) of the tunneling current I at a certain tip-surface distance z during retraction (approach) of the tip. This drop (increase) may be associated with the tunneling of a single He adatom, opening new possibilities to study the quantum tunneling of atoms via STM.     

Linearity of the Faraday-rotation-type ac magnetic-field sensor with a ferrimagnetic or ferromagnetic rotator film

We analyze the linearity and modulation depth of ac magnetic-field sensors or current sensors, using a ferrimagnetic or ferromagnetic film as the Faraday rotator and employing the detection of only the zeroth-order optical diffraction component from the rotator. It is theoretically shown that for this class of sensor the condition of a constant modulation depth and that of a constant ratio error give an identical series of curves for the relationship between Faraday rotation angle Θ and polarizer/analyzer relative angle Φ. We give some numerical examples to demonstrate the usefulness of the result with reference to a rare-earth iron garnet film as the rotator.     

Superconductivity and spin correlation study by Fe3+ - ESR in (La1−xSrx)2Cu1−yFeyO4−z

Fe³⁺-ESR measurements are carried out for the samples of (La_1–xSr_x)₂Cu_1–yFe_yO_4–z. Peak-peak width H_pp of the signals decreases with falling temperature until minimum value and rises sharply with further decreasing temperature, which is approximated by H_pp = C₀ +C₁/T + BT. The H_pp behavior at high temperature and at low temperature can be analyzed by Korringa mechanism and slowing down of Fe₃₊ spin fluctuation, respectively. From the analysis of coefficient B's of Korringa terms, C₀ and g-shift, it is revealed that the magnetic interaction of Fe³⁺ with hole carriers and Ce²⁺ spins depends strongly on hole density.     

Slicing the metric space to provide quick indexing of complex data in the main memory

Searching in a dataset for elements that are similar to a given query element is a core problem in applications that manage complex data, and has been aided by metric access methods (MAMs). A growing number of applications require indices that must be built faster and repeatedly, also providing faster response for similarity queries. The increase in the main memory capacity and its lowering costs also motivate using memory-based MAMs. In this paper, we propose the Onion-tree, a new and robust dynamic memory-based MAM that slices the metric space into disjoint subspaces to provide quick indexing of complex data. It introduces three major characteristics: (i) a partitioning method that controls the number of disjoint subspaces generated at each node; (ii) a replacement technique that can change the leaf node pivots in insertion operations; and (iii) range and k-NN extended query algorithms to support the new partitioning method, including a new visit order of the subspaces in k-NN queries. Performance tests with both real-world and synthetic datasets showed that the Onion-tree is very compact. Comparisons of the Onion-tree with the MM-tree and a memory-based version of the Slim-tree showed that the Onion-tree was always faster to build the index. The experiments also showed that the Onion-tree significantly improved range and k-NN query processing performance and was the most efficient MAM, followed by the MM-tree, which in turn outperformed the Slim-tree in almost all the tests.     

Estimation of time-dependent input from neuronal membrane potential

The set of firing rates of the presynaptic excitatory and inhibitory neurons constitutes the input signal to the postsynaptic neuron. Estimation of the time-varying input rates from intracellularly recorded membrane potential is investigated here. For that purpose, the membrane potential dynamics must be specified. We consider the Ornstein-Uhlenbeck stochastic process, one of the most common single-neuron models, with time-dependent mean and variance. Assuming the slow variation of these two moments, it is possible to formulate the estimation problem by using a state-space model. We develop an algorithm that estimates the paths of the mean and variance of the input current by using the empirical Bayes approach. Then the input firing rates are directly available from the moments. The proposed method is applied to three simulated data examples: constant signal, sinusoidally modulated signal, and constant signal with a jump. For the constant signal, the estimation performance of the method is comparable to that of the traditionally applied maximum likelihood method. Further, the proposed method accurately estimates both continuous and discontinuous time-variable signals. In the case of the signal with a jump, which does not satisfy the assumption of slow variability, the robustness of the method is verified. It can be concluded that the method provides reliable estimates of the total input firing rates, which are not experimentally measurable.     

Optimizing time histograms for non-Poissonian spike trains

The time histogram is a fundamental tool for representing the inhomogeneous density of event occurrences such as neuronal firings. The shape of a histogram critically depends on the size of the bins that partition the time axis. In most neurophysiological studies, however, researchers have arbitrarily selected the bin size when analyzing fluctuations in neuronal activity. A rigorous method for selecting the appropriate bin size was recently derived so that the mean integrated squared error between the time histogram and the unknown underlying rate is minimized (Shimazaki & Shinomoto, 2007 ). This derivation assumes that spikes are independently drawn from a given rate. However, in practice, biological neurons express non-Poissonian features in their firing patterns, such that the spike occurrence depends on the preceding spikes, which inevitably deteriorate the optimization. In this letter, we revise the method for selecting the bin size by considering the possible non-Poissonian features. Improvement in the goodness of fit of the time histogram is assessed and confirmed by numerically simulated non-Poissonian spike trains derived from the given fluctuating rate. For some experimental data, the revised algorithm transforms the shape of the time histogram from the Poissonian optimization method.