How bees distinguish black from white

Bee eyes have photoreceptors for ultraviolet, green, and blue wavelengths that are excited by reflected white but not by black. With ultraviolet reflections excluded by the apparatus, bees can learn to distinguish between black, gray, and white, but theories of color vision are clearly of no help in explaining how they succeed. Human vision sidesteps the issue by constructing black and white in the brain. Bees have quite different and accessible mechanisms. As revealed by extensive tests of trained bees, bees learned two strong signals displayed on either target. The first input was the position and a measure of the green receptor modulation at the vertical edges of a black area, which included a measure of the angular width between the edges of black. They also learned the average position and total amount of blue reflected from white areas. These two inputs were sufficient to help decide which of two targets held the reward of sugar solution, but the bees cared nothing for the black or white as colors, or the direction of contrast at black/white edges. These findings provide a small step toward understanding, modeling, and implementing in silicon the anti-intuitive visual system of the honeybee, in feeding behavior

How bees discriminate a pattern of two colours from its mirror image

A century ago, in his study of colour vision in the honeybee (Apis mellifera), Karl von Frisch showed that bees distinguish between a disc that is half yellow, half blue, and a mirror image of the same. Although his inference of colour vision in this example has been accepted, some discrepancies have prompted a new investigation of the detection of polarity in coloured patterns. In new experiments, bees restricted to their blue and green receptors by exclusion of ultraviolet could learn patterns of this type if they displayed a difference in green contrast between the two colours. Patterns with no green contrast required an additional vertical black line as a landmark. Tests of the trained bees revealed that they had learned two inputs; a measure and the retinotopic position of blue with large field tonic detectors, and the measure and position of a vertical edge or line with small-field phasic green detectors. The angle between these two was measured. This simple combination was detected wherever it occurred in many patterns, fitting the definition of an algorithm, which is defined as a method of processing data. As long as they excited blue receptors, colours could be any colour to human eyes, even white. The blue area cue could be separated from the green receptor modulation by as much as 50°. When some blue content was not available, the bees learned two measures of the modulation of the green receptors at widely separated vertical edges, and the angle between them. There was no evidence that the bees reconstructed the lay-out of the pattern or detected a tonic input to the green receptors

HORNET: High-speed Onion Routing at the Network Layer

We present HORNET, a system that enables high-speed end-to-end anonymous channels by leveraging next generation network architectures. HORNET is designed as a low-latency onion routing system that operates at the network layer thus enabling a wide range of applications. Our system uses only symmetric cryptography for data forwarding yet requires no per-flow state on intermediate nodes. This design enables HORNET nodes to process anonymous traffic at over 93 Gb/s. HORNET can also scale as required, adding minimal processing overhead per additional anonymous channel. We discuss design and implementation details, as well as a performance and security evaluation.Comment: 14 pages, 5 figure

Controlled Sequential Monte Carlo

Sequential Monte Carlo methods, also known as particle methods, are a popular set of techniques for approximating high-dimensional probability distributions and their normalizing constants. These methods have found numerous applications in statistics and related fields; e.g. for inference in non-linear non-Gaussian state space models, and in complex static models. Like many Monte Carlo sampling schemes, they rely on proposal distributions which crucially impact their performance. We introduce here a class of controlled sequential Monte Carlo algorithms, where the proposal distributions are determined by approximating the solution to an associated optimal control problem using an iterative scheme. This method builds upon a number of existing algorithms in econometrics, physics, and statistics for inference in state space models, and generalizes these methods so as to accommodate complex static models. We provide a theoretical analysis concerning the fluctuation and stability of this methodology that also provides insight into the properties of related algorithms. We demonstrate significant gains over state-of-the-art methods at a fixed computational complexity on a variety of applications

TARANET: Traffic-Analysis Resistant Anonymity at the NETwork layer

Modern low-latency anonymity systems, no matter whether constructed as an overlay or implemented at the network layer, offer limited security guarantees against traffic analysis. On the other hand, high-latency anonymity systems offer strong security guarantees at the cost of computational overhead and long delays, which are excessive for interactive applications. We propose TARANET, an anonymity system that implements protection against traffic analysis at the network layer, and limits the incurred latency and overhead. In TARANET's setup phase, traffic analysis is thwarted by mixing. In the data transmission phase, end hosts and ASes coordinate to shape traffic into constant-rate transmission using packet splitting. Our prototype implementation shows that TARANET can forward anonymous traffic at over 50~Gbps using commodity hardware

How bees distinguish colors

Behind each facet of the compound eye, bees have photoreceptors for ultraviolet, green, and blue wavelengths that are excited by sunlight reflected from the surrounding panorama. In experiments that excluded ultraviolet, bees learned to distinguish between black, gray, white, and various colors. To distinguish two targets of differing color, bees detected, learned, and later recognized the strongest preferred inputs, irrespective of which target displayed them. First preference was the position and measure of blue reflected from white or colored areas. They also learned the positions and a measure of the green receptor modulation at vertical edges that displayed the strongest green contrast. Modulation is the receptor response to contrast and was summed over the length of a contrasting vertical edge. This also gave them a measure of angular width between outer vertical edges. Third preference was position and a measure of blue modulation. When they returned for more reward, bees recognized the familiar coincidence of these inputs at that place. They cared nothing for colors, layout of patterns, or direction of contrast, even at black/white edges. The mechanism is a new kind of color vision in which a large-field tonic blue input must coincide in time with small-field phasic modulations caused by scanning vertical edges displaying green or blue contrast. This is the kind of system to expect in medium-lowly vision, as found in insects; the next steps are fresh looks at old observations and quantitative models

Consistency in eyewitness reports of aquatic "monsters"

Little work has been undertaken on the consistency/repeatabilityof reports of natural historical anomalies. Such information is usefulin understanding the reporting process associated with such accountsand distinguishing any underlying biological signal. Here we used intraclasscorrelation as a measure of consistency in descriptions of a variety of quantitative features from a large collection of firsthand accounts of apparentlyunknown aquatic animals (hereafter “monsters”) in each of two differentcases. In the first case, same observer, same encounter (sose), the correlationwas estimated from two different accounts of the same event from thesame witness. In the second case, the correlation was between two differentobservers of the same event (dose). Overall, levels of consistency weresurprisingly high, with length of monster, distance of monster to the witness,and duration of encounter varying between 0.63 and 1. Interestingly,there was no evidence that sose accounts generally had higher consistencythan dose accounts.Publisher PDFPeer reviewe

The impact of repetitive unclamped inductive switching on the electrical parameters of low-voltage trench power nMOSFETs

The impact of hot-carrier injection (HCI) due to repetitive unclamped inductive switching (UIS) on the electrical performance of low-voltage trench power n-type MOSFETs (nMOSFETs) is assessed. Trench power nMOSFETs with 20- and 30-V breakdown voltage ratings in TO-220 packages have been fabricated and subjected to over 100 million cycles of repetitive UIS with different avalanche currents IAV at a mounting base temperature TMB of 150°C. Impact ionization during avalanche conduction in the channel causes hot-hole injection into the gate dielectric, which results in a reduction of the threshold voltage VGSTX, as the number of avalanche cycles N increases. The experimental data reveal a power-law relationship between the change in the threshold voltage ΔVGSTX and N. The results show that the power-law prefactor is directly proportional to the avalanche current. After 100 million cycles, it was observed in the 20-V rated MOSFETs that the power-law prefactor increased by 30% when IAV was increased from 160 to 225 A, thereby approximating a linear relationship. A stable subthreshold slope with avalanche cycling indicates that interface trap generation may not be an active degradation mechanism. The impact of the cell pitch on avalanche ruggedness is also investigated by testing 2.5- and 4- m cell-pitch 30-V rated MOSFETs. Measurements showed that the power-law prefactor reduced by 40% when the cell pitch was reduced by 37.5%. The improved VGSTX stability with the smaller cell-pitch MOSFETs is attributed to a lower avalanche current per unit cell resulting in less hot-hole injection and, hence, smaller VGSTX shift. The 2.5-m cell-pitch MOSFETs also show 25% improved on -state resistance RDSON, better RDSON stability, and 20% less subthreshold slope compared with the 4-m cell-pitch MOSFETs, although with 100% higher initial IDSS and less IDSS stability with avalanche cycling. These results are important for manufacturers of automotive MOSFETs where multiple avalanche occurrences over the lifetime of the MOSFET are expected