Error Probability Mitigation in Quantum Reading Using Classical Codes

A general framework describing the statistical discrimination of an ensemble of quantum channels is given by the name quantum reading. Several tools can be applied in quantum reading to reduce the error probability in distinguishing the ensemble of channels. Classical and quantum codes can be envisioned for this goal. The aim of this paper is to present a simple but fruitful protocol for this task using classical error-correcting codes. Three families of codes are considered: Reed–Solomon codes, BCH codes, and Reed–Muller codes. In conjunction with the use of codes, we also analyze the role of the receiver. In particular, heterodyne and Dolinar receivers are taken into consideration. The encoding and measurement schemes are connected by the probing step. As probes, we consider coherent states. In such a simple manner, interesting results are obtained. As we show, there is a threshold below which using codes surpass optimal and sophisticated schemes for any fixed rate and code. BCH codes in conjunction with Dolinar receiver turn out to be the optimal strategy for error mitigation in quantum reading.

On the Design of Rate Adaptation for Relay-Assisted Satellite Hybrid FSO/RF Systems

<p>This paper addresses the design of hybrid free-space optical/radio frequency (FSO/RF) systems for a high-altitude platform (HAP)-aided relaying satellite communication for mobile networks supported by unmanned aerial vehicle (UAV). While prior work primarily focused on fixed-rate design, which frequently switches between FSO and RF lead to reduce the system performance, we propose a rate adaptation design that gradually adjusts the data rate in each link when its channel state fluctuates. The proposed design's downlink performance is analyzed, taking into account many challenging issues, including beam spreading loss, cloud attenuation, statistical behaviors of the atmospheric turbulence in the dual-hop channel, and pointing misalignment due to the UAV hovering. Different performance metrics are analytically derived based on channel modelings, such as outage probability, average transmission rate, achievable spectrum efficiency, and average transmission rate. The numerical results quantitatively confirm the effectiveness of our proposed system under the impact of UAV hovering misalignment and atmospheric-related issues like clouds and turbulence. Finally, Monte-Carlo simulations validate the accuracy of theoretical results.</p>

Deep Learning for seasonality modelling in Inflation-Indexed Swap pricing

An Inflation-Indexed Swap (IIS) is a derivative in which, at every payment date, the counterparties swap an inflation rate with a fixed rate. For the calculation of the Inflation Leg cash flows it is necessary to build a mathematical model suitable for the Consumer Price Index (CPI) projection. For this purpose, quants typically start by using market quotes for the Zero-Coupon swaps in order to derive the future trend of the inflation index, together with a seasonality model for capturing the typical periodical effects. In this study, we propose a forecasting model for inflation seasonality based on a Long Short Term Memory (LSTM) network: a deep learning methodology particularly useful for forecasting purposes. The CPI predictions are conducted using a FinTech paradigm, but in respect of the traditional quantitative finance theory developed in this research field. The paper is structured according to the following sections: the first two parts illustrate the pricing methodologies for the most popular IIS: the Zero Coupon Inflation-Indexed Swap (ZCIIS) and the Year-on-Year Inflation-Indexed Swap (YYIIS); section 3 deals with the traditional standard method for the forecast of CPI values (trend + seasonality), while section 4 describes the LSTM architecture, and section 5 focuses on CPI projections, also called inflation bootstrap. Then section 6 describes a robust check, implementing a traditional SARIMA model in order to improve the interpretation of the LSTM outputs; finally, section 7 concludes with a real market case, where the two methodologies are used for computing the fair-value for a YYIIS and the model risk is quantified.

Physical Modeling of Snow Gliding: A Case Study in the NW Italian Alps

Snow gliding, a slow movement downhill of snow cover, is complex to forecast and model and yet is extremely important, because it drives snowpack dynamics in the pre-avalanching phase. Despite recent interest in this process and the development of some studies therein, this phenomenon is poorly understood and represents a major point of uncertainty for avalanche forecasting. This study presents a data-driven, physically based, time-dependent 1D model, Poli-Glide, able to predict the slow movement of snowpacks along a flow line at the daily scale. The objective of the work was to create a useful snow gliding model, requiring few, relatively easily available input data, by (i) modeling snowpack evolution from measured precipitation and air temperature, (ii) evaluating the rate and extent of movement of the snowpack in the gliding phase, and (iii) assessing fracture (i.e., avalanching) timing. Such a model could be then used to provide hazard assessment in areas subject to gliding, thereby, and subsequent avalanching. To do so, some simplifying assumptions were introduced, namely that (i) negligible traction stress occurs within soil, (ii) water percolation into snow occurs at a fixed rate, and (iii) the micro topography of soil is schematized according to a sinusoidal function in the absence of soil erosion. The proposed model was then applied to the “Torrent des Marais-Mont de La Saxe” site in Aosta Valley, monitored during the winters of 2010 and 2011, featuring different weather conditions. The results showed an acceptable capacity of the model to reproduce snowpack deformation patterns and the final snowpack’s displacement. Correlation analysis based upon observed glide rates further confirmed dependence against the chosen variables, thus witnessing the goodness of the model. The results could be a valuable starting point for future research aimed at including more complex parameterizations of the different processes that affect gliding.

Experimental Study on the Performance of Rate Adaptation Algorithm in IEEE 802.11g Networks

<p>IEEE 802.11 technology provides a low-cost wireless networking solution. In the last few years, we have seen that the demand for high-bandwidth wireless local area networks increases rapidly, due to the proliferation of mobile devices such as laptops, smart phones and tablet PCs. This has driven the widespread deployment of IEEE 802.11 wireless networks to provide Internet access. However, wireless networks present their own unique problems. Wireless channel is extremely variable and can be affected by a number of different factors, such as collisions, multipath fading and signal attenuation. As such, rate adaptation algorithm is a key component of IEEE 802.11 standard which is used to vary the transmission data rate to match the wireless channel conditions, in order to achieve the best possible performance. Rate adaptation algorithm studies and evaluations are always hot research topics. However, despite its popularity, little work has been done on evaluating the performance of rate adaptation algorithms by comparing the throughput of the algorithm with the throughput of the fixed rates. This thesis presents an experimental study that compares the performance ofMikroTik rate adaptation algorithm andMinstrel rate adaptation algorithm against fixed rates in an IEEE 802.11g network. MikroTik and Minstrel rate adaptation algorithm are most commonly used algorithm around the world. All experiments are conducted in a real world environment in this thesis. In a real world environment, wireless channel conditions are not tightly being controlled, and it is extremely vulnerable to interference of surrounding environment. The dynamic changes of wireless channel conditions have a considerable effect on the performance of rate adaptation algorithms. The main challenge of evaluating a rate adaptation algorithm in a real world environment is getting different experiment behaviours from the same experiment. Experiment results may indicate many different behaviours which due to the leak of wireless environment controlling. Having a final conclusion from those experiment results can be a challenge task. In order to perform a comprehensive rate adaptation algorithm evaluation. All experiments run 20 times for 60 seconds. The average result and stand deviation is calculated. We also design and implement an automation experiment controlling program to help us maintain that each run of experiment is following exactly the same procedures. In MikroTik rate adaptation algorithm evaluation, the results show in many cases that fixed rate outperforms rate adaptation. Our findings raise questions regarding the suitability of the adopted rate adaptation algorithm in typical indoor environments. Furthermore, our study indicates that it is not wise to simply ignore fixed rate. A fine selection of a fixed rate could be made to achieve desired performance. The result ofMinstrel rate adaptation evaluation show that whilst Minstrel performs reasonably well in static wireless channel conditions, in some cases the algorithm has difficulty selecting the optimal data rate in the presence of dynamic channel conditions. In addition, Minstrel performs well when the channel condition improves frombad quality to good quality. However, Minstrel has trouble selecting the optimal rate when the channel condition deteriorates from good quality to bad quality. By comparing the experimental results between the performance of rate adaptation algorithms and the performance of fixed data rate against different factors, the experiment results directly pointed out the weakness of these two rate adaptation algorithms. Our findings from both experiments provide useful information on the design of rate adaptation algorithms.</p>

Experimental Study on the Performance of Rate Adaptation Algorithm in IEEE 802.11g Networks

<p>IEEE 802.11 technology provides a low-cost wireless networking solution. In the last few years, we have seen that the demand for high-bandwidth wireless local area networks increases rapidly, due to the proliferation of mobile devices such as laptops, smart phones and tablet PCs. This has driven the widespread deployment of IEEE 802.11 wireless networks to provide Internet access. However, wireless networks present their own unique problems. Wireless channel is extremely variable and can be affected by a number of different factors, such as collisions, multipath fading and signal attenuation. As such, rate adaptation algorithm is a key component of IEEE 802.11 standard which is used to vary the transmission data rate to match the wireless channel conditions, in order to achieve the best possible performance. Rate adaptation algorithm studies and evaluations are always hot research topics. However, despite its popularity, little work has been done on evaluating the performance of rate adaptation algorithms by comparing the throughput of the algorithm with the throughput of the fixed rates. This thesis presents an experimental study that compares the performance ofMikroTik rate adaptation algorithm andMinstrel rate adaptation algorithm against fixed rates in an IEEE 802.11g network. MikroTik and Minstrel rate adaptation algorithm are most commonly used algorithm around the world. All experiments are conducted in a real world environment in this thesis. In a real world environment, wireless channel conditions are not tightly being controlled, and it is extremely vulnerable to interference of surrounding environment. The dynamic changes of wireless channel conditions have a considerable effect on the performance of rate adaptation algorithms. The main challenge of evaluating a rate adaptation algorithm in a real world environment is getting different experiment behaviours from the same experiment. Experiment results may indicate many different behaviours which due to the leak of wireless environment controlling. Having a final conclusion from those experiment results can be a challenge task. In order to perform a comprehensive rate adaptation algorithm evaluation. All experiments run 20 times for 60 seconds. The average result and stand deviation is calculated. We also design and implement an automation experiment controlling program to help us maintain that each run of experiment is following exactly the same procedures. In MikroTik rate adaptation algorithm evaluation, the results show in many cases that fixed rate outperforms rate adaptation. Our findings raise questions regarding the suitability of the adopted rate adaptation algorithm in typical indoor environments. Furthermore, our study indicates that it is not wise to simply ignore fixed rate. A fine selection of a fixed rate could be made to achieve desired performance. The result ofMinstrel rate adaptation evaluation show that whilst Minstrel performs reasonably well in static wireless channel conditions, in some cases the algorithm has difficulty selecting the optimal data rate in the presence of dynamic channel conditions. In addition, Minstrel performs well when the channel condition improves frombad quality to good quality. However, Minstrel has trouble selecting the optimal rate when the channel condition deteriorates from good quality to bad quality. By comparing the experimental results between the performance of rate adaptation algorithms and the performance of fixed data rate against different factors, the experiment results directly pointed out the weakness of these two rate adaptation algorithms. Our findings from both experiments provide useful information on the design of rate adaptation algorithms.</p>

Integer Ambiguity Fixation Based on SC-PAR Algorithm

In terms of quality control of ambiguity estimation, the common partial ambiguity fixation algorithm is improved, and the SC-PAR (Single frequency Combined Partial Ambiguity Resolution) algorithm is proposed. After the algorithm fails to fix the full ambiguity, it filters the ambiguity subset step by step according to the number of continuous satellite lock epochs, satellite elevation angle, satellite signal-to-noise ratio, geometric precision factor, ambiguity variance and ambiguity precision attenuation factor, and searches Optimal ambiguity subset. According to the R-ratio value and the success rate index, the search results are jointly tested, and the remaining subsets are corrected with the subsets that pass the test. The results show that compared with the FAR and conventional PAR algorithms, the fixed rate of the SC-PAR algorithm is increased by 65.01% and 27.97%, respectively, and the accuracy is also significantly improved.

A Short Dynamic Scan Method of Measuring Bone Metabolic Flux Using [18F]NaF PET

[18F]NaF PET measurements of bone metabolic flux (Ki) are conventionally obtained with 60-min dynamic scans analysed using the Hawkins model. However, long scan times make this method expensive and uncomfortable for subjects. Therefore, we evaluated and compared measurements of Ki with shorter scan times analysed with fixed values of the Hawkins model rate constants. The scans were acquired in a trial in 30 postmenopausal women, half treated with teriparatide (TPT) and half untreated. Sixty-minute PET-CT scans of both hips were acquired at baseline and week 12 after injection with 180 MBq [18F]NaF. Scans were analysed using the Hawkins model by fitting bone time–activity curves at seven volumes of interest (VOIs) with a semi-population arterial input function. The model was re-run with fixed rate-constants for dynamic scan times from 0–12 min increasing in 4-min steps up to 0–60 min. Using the Hawkins model with fixed rate-constants, Ki measurements with statistical power equivalent or superior to conventionally analysed 60-min dynamic scans were obtained with scan times as short as 12 min.

The Green-Innovation-Inducing Effect of a Unit Progressive Carbon Tax

The major global economies are facing increasing pressure to reduce their carbon emissions. Introducing environmental policy instruments to stimulate green innovation is key to mitigating global warming. We propose a carbon tax design with a typical green innovation orientation that links carbon taxes with the low-carbon technology (LCT) of enterprises and imposes a progressive tax on heterogeneous enterprises with LCT stock to encourage green innovation. This study used a dynamic evolution game model based on the Stackelberg model of heterogeneous enterprises with LCT stock to analyze the green-innovation-inducing effect of unit progressive carbon taxes. A unit progressive carbon tax could encourage enterprises to participate in green innovation, regardless of their initial green innovation willingness. The progressive tax rate was more effective than a fixed rate for stimulating green innovation by all enterprises. There was a marginal diminishing effect of increases in the tax rate. An increase in the innovation cost coefficient of enterprises reduced the green-innovation-inducing effect of the unit progressive carbon tax. Increasing the tax rate was effective only under normal circumstances. A decline in the carbon reduction in enterprises also reduced the green-innovation-inducing effect of the unit progressive carbon tax. Furthermore, increasing the tax rate when the carbon reduction amount was extremely low caused enterprises to abandon green innovation.