Radiative-Recoil Corrections of Order α(Zα)5(m/M)m\alpha(Z\alpha)^5(m/M)m to Lamb Shift Revisited

The results and main steps of an analytic calculation of radiative-recoil corrections of order $\alpha(Z\alpha)^5(m/M)m$ to the Lamb shift in hydrogen are presented. The calculations are performed in the infrared safe Yennie gauge. The discrepancy between two previous numerical calculations of these corrections existing in the literature is resolved. Our new result eliminates the largest source of the theoretical uncertainty in the magnitude of the deuterium-hydrogen isotope shift.Comment: 14 pages, REVTE

Answer Set Programming Modulo `Space-Time'

We present ASP Modulo `Space-Time', a declarative representational and computational framework to perform commonsense reasoning about regions with both spatial and temporal components. Supported are capabilities for mixed qualitative-quantitative reasoning, consistency checking, and inferring compositions of space-time relations; these capabilities combine and synergise for applications in a range of AI application areas where the processing and interpretation of spatio-temporal data is crucial. The framework and resulting system is the only general KR-based method for declaratively reasoning about the dynamics of `space-time' regions as first-class objects. We present an empirical evaluation (with scalability and robustness results), and include diverse application examples involving interpretation and control tasks

Environmental effects on the tensile strength of chemically vapor deposited silicon carbide fibers

The room temperature and elevated temperature tensile strengths of commercially available chemically vapor-deposited (CVD) silicon carbide fibers were measured after 15 min heat treatment to 1600 C in various environments. These environments included oxygen, air, argon and nitrogen at one atmosphere and vacuum at 10/9 atmosphere. Two types of fibers were examined which differed in the SiC content of their carbon-rich coatings. Threshold temperature for fiber strength degradation was observed to be dependent on the as-received fiber-flaw structure, on the environment and on the coating. Fractographic analyses and flexural strength measurements indicate that tensile strength losses were caused by surface degradation. Oxidation of the surface coating is suggested as one possible degradation mechanism. The SiC fibers containing the higher percentage of SiC near the surface of the carbon-rich coating show better strength retention and higher elevated temperature strength

Hopping Conduction in Uniaxially Stressed Si:B near the Insulator-Metal Transition

Using uniaxial stress to tune the critical density near that of the sample, we have studied in detail the low-temperature conductivity of p-type Si:B in the insulating phase very near the metal-insulator transition. For all values of temperature and stress, the conductivity collapses onto a single universal scaling curve. For large values of the argument, the scaling function is well fit by the exponentially activated form associated with variable range hopping when electron-electron interactions cause a soft Coulomb gap in the density of states at the Fermi energy. The temperature dependence of the prefactor, corresponding to the T-dependence of the critical curve, has been determined reliably for this system, and is proportional to the square-root of T. We show explicitly that nevlecting the prefactor leads to substantial errors in the determination of the scaling parameters and the critical exponents derived from them. The conductivity is not consistent with Mott variable-range hopping in the critical region nor does it obey this form for any range of the parameters. Instead, for smaller argument of the scaling function, the conductivity of Si:B is well fit by an exponential form with exponent 0.31 related to the critical exponents of the system at the metal- insulator transition.Comment: 13 pages, 6 figure

Machine Learning based Cryptocurrency Price Prediction using historical data and Social Media Sentiment

The purpose of this research is to investigate the impact of social media sentiments on predicting the Bitcoin price using machine learning models, with a focus on integrating on-chain data and employing a Multi Modal Fusion Model. For conducting the experiments, the crypto market data, on-chain data, and corresponding social media data (Twitter) has been collected from 2014 to 2022 containing over 2000 samples. We trained various models over historical data including K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Extreme Gradient Boosting and a Multi Modal Fusion. Next, we added Twitter sentiment data to the models, using the Twitter-roBERTa and VADAR models to analyse the sentiments expressed in social media about Bitcoin. We then compared the performance of these models with and without the Twitter sentiment data and found that the inclusion of sentiment feature resulted in consistently better performance, with Twitter-RoBERTa-based sentiment giving an average F1 scores of 0.79. The best performing model was an optimised Multi Modal Fusion classifier using Twitter-RoBERTa based sentiment, producing an F1 score of 0.85. This study represents a significant contribution to the field of financial forecasting by demonstrating the potential of social media sentiment analysis, on-chain data integration, and the application of a Multi Modal Fusion model to improve the accuracy and robustness of machine learning models for predicting market trends, providing a valuable tool for investors, brokers, and traders seeking to make informed decisions

Left inferior frontal gyrus connectivity with the dorsomedial subsystem of default network tracks real-world conversation behaviour

Social interactions are multifaceted, complex, and critical to social behaviour as they help gather information, develop social connections, and regulate social behaviour (Lakey & Orehek, 2011; Testard et al., 2021; Jolly & Chang, 2021). Among social interactions, conversations find a special place for humans due to the nuances associated with language, conversational behaviour (e.g., gestures), and context (e.g., where conversations occur and what is discussed). Researchers have studied aspects of single conversation behaviour, content related to conversations, and brain function (Sievers et al., 2020). However, little is known about the brain function of densely-sampled in-person conversation behaviour. Filling this gap is important, given that real-world conversation happens frequently and is an index of social connectedness. We utilise the passive-mobile sensing approach from the StudentLife study (Wang et al., 2014; daSilva et al., 2021) to track real-world conversations and relate the features to resting-state functional connectivity via fMRI. In this thesis, we show that resting state functional connectivity of left inferior frontal gyrus (LIFG, a region associated with language; Turken & Dronkers, 2011; Klaus & Hartwigsen, 2019) with the dorsomedial prefrontal cortex (dMPFC) subsystem of the default mode network (DMN), which is a network associated with social-cognitive processes (Collier & Meyer, 2020; Sippel et al., 2021), of an individual is related to the time they spend in the vicinity of conversations. Consistent with social psychological literature (Delormier, Frohlich, & Potvin, 2009; Dunbar, 2017), we also find that features of conversation – average time spent, the variance associated with, and total time spent around conversations – at places associated with ‘social eating’ was related to the same brain function. Our results suggest that the importance of LIFG within the dMPFC subsystem may be associated with (1) average time spent around conversations generally, and (2) conversations occurring specifically in socially relevant situations. This thesis also supports that passive-mobile sensing can be useful to study real-world conversations, and that adding neuroimaging modalities to otherwise densely-sampled behavioural features can open new avenues of research to better understand the brain-basis of social interactions