Traffic Speed Prediction Based on Heterogeneous Graph Attention Residual Time Series Convolutional Networks

Accurate and timely traffic forecasting is an important task for the realization of urban smart traffic. The random occurrence of social events such as traffic accidents will make traffic prediction particularly difficult. At the same time, most of the existing prediction methods rely on prior knowledge to obtain traffic maps and the obtained map structure cannot be guaranteed to be accurate for the current learning task. In addition, traffic data is highly non-linear and long-term dependent, so it is more difficult to achieve accurate prediction. In response to the above problems, this paper proposes a new integrated unified architecture for traffic prediction based on heterogeneous graph attention network combined with residual-time-series convolutional network, which is called HGA-ResTCN. First, the heterogeneous graph attention is used to capture the changes in the relationship between the traffic graph nodes caused by social events, so as to learn the link weights between the target node and its neighbor nodes; at the same time, by introducing the timing of residual links convolutional network to capture the long-term dependence of complex traffic data. These two models are integrated into a unified framework to learn in an end-to-end manner. Through testing on real-world data sets, the results show that the accuracy of the model in this paper is better than other proposed baselines.

Co-Spray Dried Nafamostat Mesylate with Lecithin and Mannitol as Respirable Microparticles for Targeted Pulmonary Delivery: Pharmacokinetics and Lung Distribution in Rats

Coronavirus disease 2019 (COVID-19), caused by a new strain of coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is spreading rapidly worldwide. Nafamostat mesylate (NFM) suppresses transmembrane serine protease 2 and SARS-CoV-2 S protein-mediated fusion. In this study, pharmacokinetics and lung distribution of NFM, administered via intravenous and intratracheal routes, were determined using high performance liquid chromatography analysis of blood plasma, lung lumen using bronchoalveolar lavage fluid, and lung tissue. Intratracheal administration had higher drug delivery and longer residual time in the lung lumen and tissue, which are the main sites of action, than intravenous administration. We confirmed the effect of lecithin as a stabilizer through an ex vivo stability test. Lecithin acts as an inhibitor of carboxylesterase and delays NFM decomposition. We prepared inhalable microparticles with NFM, lecithin, and mannitol via the co-spray method. The formulation prepared using an NFM:lecithin:mannitol ratio of 1:1:100 had a small particle size and excellent aerodynamic performance. Spray dried microparticles containing NFM, lecithin, and mannitol (1:1:100) had the longest residual time in the lung tissue. In conclusion, NFM-inhalable microparticles were prepared and confirmed to be delivered into the respiratory tract, such as lung lumen and lung tissue, through in vitro and in vivo evaluations.

Determination of the correlation degree between GNSS stations of Ukraine based on time series

Using GNSS for many years is the most common technology for the collection, processing, and interpretation of Earth observation data, in particular for the high-precision study of plate tectonics. The results of GNSS observations, such as coordinate time series, allow us to do continuous monitoring of stations, and modern methods of satellite observation processing provide high-precision results for geodynamic interpretation. The aim of our study is to process the results of observations by DD and PPP methods and determine the degree of correlation between GNSS stations based on coordinate time series. For our study, we selected 10 GNSS stations, which merged into two networks - Lviv (SAMB, STOY, STRY, SULP та ZLRS) and Ukrainian (BCRV, CHTK, CNIV, CRNI, GLSV та SULP). The duration of observations on each of them is about 1.5 years (2019-2020). The downloaded observation files were processed in two software packages: Gamit and GipsyX. After applying the «cleaned» procedures based on the iGPS software package, the residual time series were obtained and the coefficients of the interstation correlation matrices were calculated. After the procedure of "cleaning" the time series, we obtained the RMS value decrease for all components of the coordinates by an average of 7-30%, and some stations by 55%. Based on the obtained RMS values, we can conclude that the influence of unextracted or incorrectly modeled errors can significantly affect the results of satellite observations. The obtained interstation correlation coefficients for both networks show different results depending on the used method for processing satellite observations. The larger correlation values of the DD method can be explained by the fact that the effect of errors is distributed evenly to all network stations, whereas in the PPP method errors for each station are individual. The obtained graphs of the common-mode errors values, after their removal from the residual time series, confirm the more uniform nature of the DD method. The results of our study indicate the feasibility of using the PPP method, as the autonomous processing of stations allows you to see the real geodynamic picture of the studied region.

Time-interleaved pulse-shrinking and all-digital time-to-digital converters

This dissertation deals with the design of sub-per-stage delay time-to-digital converters (TDCs). Two classes of TDCs namely pulse-shrinking TDCs and TDCs are investigated. In pulse-shrinking TDCs, a two-step pulse-shrinking TDC consisting of a set of coarse and fine pulse-shrinking TDCs is proposed to increase a dynamic range without employing a large number of pulse-shrinking stages. A residual time extraction scheme capable of extracting the residual time of the coarse TDC is developed. The simulation / measurement results of the TDC implemented in an IBM 130 nm 1.2 V CMOS technology show that the TDC offers 1.4 ns conversion time, 1LSB DNL and INL, and consumes 0.163 pJ/step. To further improve the conversion time, a time-interleaved scheme is developed to extract the residual time of the coarse TDC and utilized in design of a two-step pulse-shrinking TDC. Residual time extraction is carried out in parallel with digitization of minimize latency. The simulation and measurement results of the TDC show that is offers 0.85 ns conversion time, 0.285 LSB DNL, and 0.78 LSB. In TDCs, a 1-1 multi-stage noise shaping (MASH) TDC with a new differential cascode time integrator is proposed to suppress even-order harmonic tones and current mismatch-induced timing errors. Simulation results show that the proposed TDC offers 1.9 ps time resolution over 48-415 kHz signal band with consuming 5-2 uW. Finally , an all-digital first-order TDC utilizing a bi-directional gated delay line integrator is developed. Time integration is obtained via the accumulation of charge of the load capacitor of gated delay stages and the logic state of gated delay stages. The elimination of analog components allows the TDC to benefit fully from technology scaling. Simulation results show that the TDC offers firs-order noise-shaping, 10.8 ps time resolution while consuming 46 uW.

Time-interleaved pulse-shrinking and all-digital time-to-digital converters

This dissertation deals with the design of sub-per-stage delay time-to-digital converters (TDCs). Two classes of TDCs namely pulse-shrinking TDCs and TDCs are investigated. In pulse-shrinking TDCs, a two-step pulse-shrinking TDC consisting of a set of coarse and fine pulse-shrinking TDCs is proposed to increase a dynamic range without employing a large number of pulse-shrinking stages. A residual time extraction scheme capable of extracting the residual time of the coarse TDC is developed. The simulation / measurement results of the TDC implemented in an IBM 130 nm 1.2 V CMOS technology show that the TDC offers 1.4 ns conversion time, 1LSB DNL and INL, and consumes 0.163 pJ/step. To further improve the conversion time, a time-interleaved scheme is developed to extract the residual time of the coarse TDC and utilized in design of a two-step pulse-shrinking TDC. Residual time extraction is carried out in parallel with digitization of minimize latency. The simulation and measurement results of the TDC show that is offers 0.85 ns conversion time, 0.285 LSB DNL, and 0.78 LSB. In TDCs, a 1-1 multi-stage noise shaping (MASH) TDC with a new differential cascode time integrator is proposed to suppress even-order harmonic tones and current mismatch-induced timing errors. Simulation results show that the proposed TDC offers 1.9 ps time resolution over 48-415 kHz signal band with consuming 5-2 uW. Finally , an all-digital first-order TDC utilizing a bi-directional gated delay line integrator is developed. Time integration is obtained via the accumulation of charge of the load capacitor of gated delay stages and the logic state of gated delay stages. The elimination of analog components allows the TDC to benefit fully from technology scaling. Simulation results show that the TDC offers firs-order noise-shaping, 10.8 ps time resolution while consuming 46 uW.

Separation of Structural and Measurement Uncertainties in Watershed Hydrological Models

<p>A hydrological model incurs three types of uncertainties: measurement, structural and parametric uncertainty. Measurement uncertainty exists due to errors in the measurements of rainfall and streamflow data. Structural uncertainty exists due to errors in the mathematical representation of hydrological processes. Parametric uncertainty is a consequence of limited data available to calibrate the model, and measurement and structural uncertainties.</p><p>Recently, separation of structural and measurement uncertainties was identified as one of the twenty-three unsolved problems in hydrology. The information about measurement and structural uncertainties is typically available in the form of residual time-series, that is, the difference between observed and simulated streamflow time-series. The residual time-series, however, provides only an aggregate measure of measurement and structural uncertainties. Thus, the measurement and structural uncertainties are inseparable without additional information. In this study, we used random forest (RF) algorithm to gather additional information about measurement uncertainties using hydrological data across several watersheds. Subsequently, the uncertainty bounds obtained by RF were compared against the uncertainty bounds obtained by two other methods: rating-curve analysis and recently proposed runoff-coefficient method. Rating curve analysis yields uncertainty in streamflow measurements only and the runoff-coefficient yields uncertainty in both rainfall and streamflow measurements. The results of the study are promising in terms of using data across different watersheds for the construction of measurement uncertainty bounds. The preliminary results of this study will be presented in the meeting.</p>

Bubble needs strings

In this paper, we want to emphasize the pivotal role played by strings in the model realizing de Sitter using an expanding bubble, proposed and subsequently developed in [1–3]. Contrary to the Randall-Sundrum model of brane-localized gravity, we use the end points of radially stretched strings to obtain matter sourcing gravity induced on the bubble wall. This allows us to reinterpret the possible volume divergence coming from naive dimensional reduction as mass renormalization in four dimensional particle physics. Furthermore, we argue that the residual time dependence in the bulk, pointed out by some recent work as a possible shortcoming of such models, is automatically cured in presence of these stringy sources.

Application of Lévy processes in modelling (geodetic) time series with mixed spectra

Recently, various models have been developed, including the fractional Brownian motion (fBm), to analyse the stochastic properties of geodetic time series together with the estimated geophysical signals. The noise spectrum of these time series is generally modelled as a mixed spectrum, with a sum of white and coloured noise. Here, we are interested in modelling the residual time series after deterministically subtracting geophysical signals from the observations. This residual time series is then assumed to be a sum of three stochastic processes, including the family of Lévy processes. The introduction of a third stochastic term models the remaining residual signals and other correlated processes. Via simulations and real time series, we identify three classes of Lévy processes, namely Gaussian, fractional and stable. In the first case, residuals are predominantly constituted of short-memory processes. The fractional Lévy process can be an alternative model to the fBm in the presence of long-term correlations and self-similarity properties. The stable process is here restrained to the special case of infinite variance, which can be only satisfied in the case of heavy-tailed distributions in the application to geodetic time series. Therefore, the model implies potential anxiety in the functional model selection, where missing geophysical information can generate such residual time series.