Web Users\u27 Implicit Feedback Inference Based on Amount of Simple Interaction Logs at Browser Side

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Analog Realization of Arbitrary One-Dimensional Maps

An increasing number of applications of a one-dimensional (1-D) map as an information processing element are found in the literature on artificial neural networks, image processing systems, and secure communication systems. In search of an efficient hardware implementation of a 1-D map, we discovered that the bifurcating neuron (BN), which was introduced elsewhere as a mathematical model of a biological neuron under the influence of an external sinusoidal signal, could provide a compact solution. The original work on the BN indicated that its firing time sequence, when it was subject to a sinusoidal driving signal, was related to the sine-circle map, suggesting that the BN can compute the sine-circle map. Despite its rich array of dynamical properties, the mathematical description of the BN is simple enough to lend itself to a compact circuit implementation. In this paper, we generalize the original work and show that the computational power of the BN can be extended to compute an arbitrary 1-D map. Also, we describe two possible circuit models of the BN: the programmable unijunction transistor oscillator neuron, which was introduced in the original work as a circuit model of the BN, and the integrated-circuit relaxation oscillator neuron (IRON), which was developed for more precise modeling of the BN. To demonstrate the computational power of the BN, we use the IRON to generate the bifurcation diagrams of the sine-circle map, the logistic map, as well as the tent map, and then compare them with exact numerical versions. The programming of the BN to compute an arbitrary map can be done simply by changing the waveform of the driving signal, which is given to the BN externally; this feature makes the circuit models of the BN especially useful in the circuit implementation of a network of 1-D maps

Prevalence of type-specific oncogenic human papillomavirus infection assessed by HPV E6/E7 mRNA among women with high-grade cervical lesions

SummaryObjectivesHuman papillomavirus (HPV) infection is a major cause of premalignant dysplasia and cervical cancer. There are no data on the prevalence of genotype-specific HPV infection assessed by HPV E6/E7 mRNA in women representative of the Korean population across a broad age range.MethodsA total of 630 women aged 17–90 years were enrolled in this study. ThinPrep liquid-based cytology samples were evaluated using the CervicGen HPV RT-qDx assay, which detects 16 high-risk (HR) HPV genotypes (set 1: HPV 16, 31, 33, 35, 52, and 58; set 2: HPV 18, 39, 45, 51, 59, and 68; and set 3: HPV 53, 56, 66, and 69).ResultsThe overall prevalence of HPV infection was 33.2% (n=209), and oncogenic high-risk HPV was detected in 75.9% (n=107) of 141 women with high-grade cervical lesions. HPV 16 was the most common HPV genotype among women with high-grade cervical lesions and histologically confirmed cervical intraepithelial neoplasia grade 2 and above (CIN2+) in the Republic of Korea (41.6%). Among women aged over 30 years, 182/329 (55%) had invasive cervical cancer and 135 (74%) of these were infected with oncogenic HR-HPV types (in particular 25% with HPV 16). Among patients diagnosed with CIN2+, the positivity rate of HR-HPV was the highest in women aged 40–49 years.ConclusionsThese results suggest that the determination of specific HPV genotypes is very important for evaluating the potential impact of preventive measures, including the use of prophylactic vaccines, on reducing the burden of cervical cancer

The bifurcating neuron networks

Among many newly raised issues in neuroscience, we have been particularly interested in three issues, time coding, the role of coherent activities, and the role of chaotic activities. The Bifurcating Neuron (BN) is our model neuron designed with these three issues in mind: it is a chaotic model that can deal with time coding and has a built-in mechanism to incorporate the influence of coherent activity in its environment. The Bifurcating Neuron Network 1 (BNN-1) is a binary associative memory based on chaotic attractors. The BNN-1, utilizing the bistability of the BN controlled by attractor-merging crisis, was shown to have a better recall ability than the continuous-time Hopfield network. The BNN-1 is particularly suited for circuit realization because the only required circuit components are relaxation oscillators and harmonic oscillators. Another feature of the BNN-1 is that its chaotic activity is self-organizing: it starts in a maximally chaotic state and settles down to a less chaotic state as a recall process proceeds. The self-organizing behavior turned out to be useful when the BNN-1 was used to solve an optimization problem: the BNN-1 could reach a solution without any external control of a network parameter. The BN Network 2 (BNN-2) is another BN network that is designed to store analog patterns. It is based on the amplitude-to-phase transformation characteristics of the BN and the constructive interference, in the sense of wave optics, among neuronal spikes. A Hebbian learning scheme results in the formation of attractors with large basins of attraction. Also, the firing-time pattern of BNs induced by the same input pattern becomes different when the frequency of the relaxation level oscillation changes, and this led us to consider the possibility of volume-holographic memory. In a numerical simulation, we could configure the BNN-2 to maintain memories of two sets of patterns, one of which becomes accessible when the frequency of the relaxation level oscillation is tuned to that of the recording phase

The bifurcating neuron networks

Among many newly raised issues in neuroscience, we have been particularly interested in three issues, time coding, the role of coherent activities, and the role of chaotic activities. The Bifurcating Neuron (BN) is our model neuron designed with these three issues in mind: it is a chaotic model that can deal with time coding and has a built-in mechanism to incorporate the influence of coherent activity in its environment. The Bifurcating Neuron Network 1 (BNN-1) is a binary associative memory based on chaotic attractors. The BNN-1, utilizing the bistability of the BN controlled by attractor-merging crisis, was shown to have a better recall ability than the continuous-time Hopfield network. The BNN-1 is particularly suited for circuit realization because the only required circuit components are relaxation oscillators and harmonic oscillators. Another feature of the BNN-1 is that its chaotic activity is self-organizing: it starts in a maximally chaotic state and settles down to a less chaotic state as a recall process proceeds. The self-organizing behavior turned out to be useful when the BNN-1 was used to solve an optimization problem: the BNN-1 could reach a solution without any external control of a network parameter. The BN Network 2 (BNN-2) is another BN network that is designed to store analog patterns. It is based on the amplitude-to-phase transformation characteristics of the BN and the constructive interference, in the sense of wave optics, among neuronal spikes. A Hebbian learning scheme results in the formation of attractors with large basins of attraction. Also, the firing-time pattern of BNs induced by the same input pattern becomes different when the frequency of the relaxation level oscillation changes, and this led us to consider the possibility of volume-holographic memory. In a numerical simulation, we could configure the BNN-2 to maintain memories of two sets of patterns, one of which becomes accessible when the frequency of the relaxation level oscillation is tuned to that of the recording phase

The bifurcating neuron networks

Among many newly raised issues in neuroscience, we have been particularly interested in three issues, time coding, the role of coherent activities, and the role of chaotic activities. The Bifurcating Neuron (BN) is our model neuron designed with these three issues in mind: it is a chaotic model that can deal with time coding and has a built-in mechanism to incorporate the influence of coherent activity in its environment. The Bifurcating Neuron Network 1 (BNN-1) is a binary associative memory based on chaotic attractors. The BNN-1, utilizing the bistability of the BN controlled by attractor-merging crisis, was shown to have a better recall ability than the continuous-time Hopfield network. The BNN-1 is particularly suited for circuit realization because the only required circuit components are relaxation oscillators and harmonic oscillators. Another feature of the BNN-1 is that its chaotic activity is self-organizing: it starts in a maximally chaotic state and settles down to a less chaotic state as a recall process proceeds. The self-organizing behavior turned out to be useful when the BNN-1 was used to solve an optimization problem: the BNN-1 could reach a solution without any external control of a network parameter. The BN Network 2 (BNN-2) is another BN network that is designed to store analog patterns. It is based on the amplitude-to-phase transformation characteristics of the BN and the constructive interference, in the sense of wave optics, among neuronal spikes. A Hebbian learning scheme results in the formation of attractors with large basins of attraction. Also, the firing-time pattern of BNs induced by the same input pattern becomes different when the frequency of the relaxation level oscillation changes, and this led us to consider the possibility of volume-holographic memory. In a numerical simulation, we could configure the BNN-2 to maintain memories of two sets of patterns, one of which becomes accessible when the frequency of the relaxation level oscillation is tuned to that of the recording phase

Investigating the information transfer efficiency of a 3 ?? 3 watch-back tactile display

A watch-back tactile display (WBTD) is expected to be a viable supplement to the user interface limitations of a smartwatch. However, its design requires that many design parameters such as tactor types and stimulus patterns be determined. We conducted a series of experiments to explore the design space of a WBTD consisting of 3??3 tactors. We demonstrated that tactor types and the temporal patterns and locus of a stimulus produce statistically significant effects on the efficiency of a WBTD. The experimental results can act as a practical guideline for the design of an efficient WBTD