A high-fidelity photon gun: intensity-squeezed light from a single molecule

A two-level atom cannot emit more than one photon at a time. As early as the 1980s, this quantum feature was identified as a gateway to "single-photon sources", where a regular excitation sequence would create a stream of light particles with photon number fluctuations below the shot noise. Such an intensity squeezed beam of light would be desirable for a range of applications such as quantum imaging, sensing, enhanced precision measurements and information processing. However, experimental realizations of these sources have been hindered by large losses caused by low photon collection efficiencies and photophysical shortcomings. By using a planar metallo-dielectric antenna applied to an organic molecule, we demonstrate the most regular stream of single photons reported to date. Measured intensity fluctuations reveal 2.2 dB squeezing limited by our detection efficiency, equivalent to 6.2 dB intensity squeezing right after the antenna.Comment: 9 pages, 3 figure

Measurement of low concentration and nano quantity hydrogen sulfide by carbon nanotube

Traditionally, hydrogen sulfide (H2S) has been regarded as toxic. It can affect the various human systems and even cause death. However, research in the 1990’s has shown that H2S can be endogenously generated by many cells and tissues in mammalian bodies, and that H2S also may play physiological roles such as those of neuromodulator and vasorelaxant in the biological system. As such, the precise measurement of the amount of H2S in a mammalian body has generated researchers’ strong interest. The ultimate goal of such a measurement should be conducted in-vivo and in real time.The existing methods for H2S measurement require both a large quantity of tissue samples and a complex procedure, so they are not highly practicable for the purpose of achieving the aforementioned goal. In this dissertation, a new method that uses carbon nanotube as an absorbent or transducer and laser-based microscopy techniques (Raman and confocal laser scanning microscopy) as signal excitation and acquisition is proposed and developed. Experimental studies are described of using this new method for analysis of both distilled water samples and serum samples in which a group of proteins are present. The study concludes that the new method (1) can measure H2S in water solutions down to a low level of concentration of 10 µM, (2) can measure H2S in sera down to a low concentration of approximately 20 µM), and (3) has a high feasibility for being used in the clinical context. Regarding (3), this is confirmed by presenting a control system that allows the laser microscopy to track carbon nanotube in a solution that has Brownian motion.While not having reached the ultimate goal as mentioned above, this work advances the state-of-the-art of the measurement of low concentration and nano-quantity of H2S in water and serum samples, in particular providing a promise toward a real-time and in-vivo H2S measurement

Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks

Event sequence, asynchronously generated with random timestamp, is ubiquitous among applications. The precise and arbitrary timestamp can carry important clues about the underlying dynamics, and has lent the event data fundamentally different from the time-series whereby series is indexed with fixed and equal time interval. One expressive mathematical tool for modeling event is point process. The intensity functions of many point processes involve two components: the background and the effect by the history. Due to its inherent spontaneousness, the background can be treated as a time series while the other need to handle the history events. In this paper, we model the background by a Recurrent Neural Network (RNN) with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics. The whole model with event type and timestamp prediction output layers can be trained end-to-end. Our approach takes an RNN perspective to point process, and models its background and history effect. For utility, our method allows a black-box treatment for modeling the intensity which is often a pre-defined parametric form in point processes. Meanwhile end-to-end training opens the venue for reusing existing rich techniques in deep network for point process modeling. We apply our model to the predictive maintenance problem using a log dataset by more than 1000 ATMs from a global bank headquartered in North America.Comment: Accepted at Thirty-First AAAI Conference on Artificial Intelligence (AAAI17

Emergence of Blind Areas in Information Spreading

Recently, contagion-based (disease, information, etc.) spreading on social networks has been extensively studied. In this paper, other than traditional full interaction, we propose a partial interaction based spreading model, considering that the informed individuals would transmit information to only a certain fraction of their neighbors due to the transmission ability in real-world social networks. Simulation results on three representative networks (BA, ER, WS) indicate that the spreading efficiency is highly correlated with the network heterogeneity. In addition, a special phenomenon, namely \emph{Information Blind Areas} where the network is separated by several information-unreachable clusters, will emerge from the spreading process. Furthermore, we also find that the size distribution of such information blind areas obeys power-law-like distribution, which has very similar exponent with that of site percolation. Detailed analyses show that the critical value is decreasing along with the network heterogeneity for the spreading process, which is complete the contrary to that of random selection. Moreover, the critical value in the latter process is also larger that of the former for the same network. Those findings might shed some lights in in-depth understanding the effect of network properties on information spreading