An improved state filter algorithm for SIR epidemic forecasting

In epidemic modeling, state filtering is an excellent tool for enhancing the performance of traditional epidemic models. We introduce a novel state filter algorithm to further improve the performance of state-of-the-art approaches based on Susceptible-Infected-Recovered (SIR) models. The proposed algorithm merges two techniques, which are typically used separately: linear correction, as seen in the Ensemble Kalman Filter (EnKF), and resampling, as used in the Particle Filter (PF). We compare the inferential accuracy of our approach against the EnKF and the Ensemble Adjustment Kalman Filter (EAKF), using algorithms employing both an uncentered co-variance matrix (UCM) and the standard column-centered covariance matrix (CCM). Our algorithm requires O(DN) more time than EnKF does, where D is the ensemble dimension and N denotes the ensemble size. We demonstrate empirically that our algorithm with UCM achieves the lowest root-mean-square-error (RMSE) and the highest correlation coefficient (CORR) amongst the selected methods, in 11 out of 14 major real-world scenarios. We show that the EnKF with UCM outperforms the EnKF with CCM, while the EAKF gains better accuracy with CCM in most scenarios

Pair density wave facilitated by Bloch quantum geometry in nearly flat band multiorbital superconductors

Bloch electrons in multiorbital systems carry quantum geometric information characteristic of their wavevector-dependent interorbital mixing. The geometric nature impacts electromagnetic responses, and this effect carries over to the superconducting state, which receives a geometric contribution to the superfluid weight. In this paper, we show that this contribution could become negative under certain appropriate circumstances. This may facilitate the stabilization of Cooper pairings with real space phase modulation, i.e. the pair density wave order, as we demonstrate through two-orbital model Bogoliubov de-Gennes mean-field calculations. The quantum geometric effect therefore constitutes an intrinsic mechanism for the formation of such a novel phase of matter in the absence of external magnetic field.Comment: 5+2 pages, 2 figures. Corrected some typos. Version accepted by Science China: Physics, Mechanics and Astronom

The Utility of "Even if..." Semifactual Explanation to Optimise Positive Outcomes

When users receive either a positive or negative outcome from an automated system, Explainable AI (XAI) has almost exclusively focused on how to mutate negative outcomes into positive ones by crossing a decision boundary using counterfactuals (e.g., \textit{"If you earn 2k more, we will accept your loan application"}). Here, we instead focus on \textit{positive} outcomes, and take the novel step of using XAI to optimise them (e.g., \textit{"Even if you wish to half your down-payment, we will still accept your loan application"}). Explanations such as these that employ "even if..." reasoning, and do not cross a decision boundary, are known as semifactuals. To instantiate semifactuals in this context, we introduce the concept of \textit{Gain} (i.e., how much a user stands to benefit from the explanation), and consider the first causal formalisation of semifactuals. Tests on benchmark datasets show our algorithms are better at maximising gain compared to prior work, and that causality is important in the process. Most importantly however, a user study supports our main hypothesis by showing people find semifactual explanations more useful than counterfactuals when they receive the positive outcome of a loan acceptance

An Econometric Model of Credit Spreads with Rebalancing, ARCH and Jump Effects

In this paper, we examine the dynamic behavior of credit spreads on corporate bond portfolios. We propose an econometric model of credit spreads that incorporates portfolio rebalancing, the near unit root property of spreads, the autocorrelation in spread changes, the ARCH conditional heteroscedasticity, jumps, and lagged market factors. In particular, our model is the first that takes into account explicitly the impact of rebalancing and yields estimates of the absorbing bounds on credit spreads induced by such rebalancing. We apply our model to nine Merrill Lynch daily series of option-adjusted spreads with ratings from AAA to C for the period January 1997 through August 2002. We find no evidence of mean reversion in these credit-spread series over our sample period. However, we find ample evidence of both the ARCH effect and jumps in the data especially in the investment-grade credit spread indices. Incorporating jumps into the ARCH type conditional variance results in significant improvements in model diagnostic tests. We also find that while log spread variations depend on both the lagged Russell 2000 index return and lagged changes in the slope of the yield curve, the time-varying jump intensity of log credit spreads is correlated with the lagged stock market volatility. Finally, our results indicate the ARCH-jump specification outperforms the ARCH specification in the out-of-sample, one-step-ahead forecast of credit spreads

An Econometric Model of Credit Spreads with Rebalancing, ARCH and Jump Effects

In this paper, we examine the dynamic behavior of credit spreads on corporate bond portfolios. We propose an econometric model of credit spreads that incorporates portfolio rebalancing, the near unit root property of spreads, the autocorrelation in spread changes, the ARCH conditional heteroscedasticity, jumps, and lagged market factors. In particular, our model is the first that takes into account explicitly the impact of rebalancing and yields estimates of the absorbing bounds on credit spreads induced by such rebalancing. We apply our model to nine Merrill Lynch daily series of option-adjusted spreads with ratings from AAA to C for the period January 1997 through August 2002. We find no evidence of mean reversion in these credit-spread series over our sample period. However, we find ample evidence of both the ARCH effect and jumps in the data especially in the investment-grade credit spread indices. Incorporating jumps into the ARCH type conditional variance results in significant improvements in model diagnostic tests. We also find that while log spread variations depend on both the lagged Russell 2000 index return and lagged changes in the slope of the yield curve, the time-varying jump intensity of log credit spreads is correlated with the lagged stock market volatility. Finally, our results indicate the ARCH-jump specification outperforms the ARCH specification in the out-of-sample, one-step-ahead forecast of credit spreads

An Economeic Model of Credit Spreads with Rebalancing, ARCH and Jump Effects

In this paper, we examine the dynamic behavior of credit spreads on corporate bond portfolios. We propose an economeic model of credit spreads that incorporates portfolio rebalancing, the near unit root property of spreads, the autocorrelation in spread changes, the ARCH conditional heteroscedasticity, jumps, and lagged market factors. In particular, our model is the first that takes into account explicitly the impact of rebalancing and yields estimates of the absorbing bounds on credit spreads induced by such rebalancing. We apply our model to nine Merrill Lynch daily series of option-adjusted spreads with ratings from AAA to C for the period January, 1997 through August, 2002. We find no evidence of mean reversion in these credit spread series over our sample period. However, we find ample evidence of both the ARCH effect and jumps in the data especially in the investment-grade credit spread indices. Incorporating jumps into the ARCH type conditional variance results in significant improvements in model diagnostic tests. We also find that while log spread variations depend on both the lagged Russell 2000 index return and lagged changes in the slope of the yield curve, the time-varying jump intensity of log credit spreads is correlated with the lagged stock market volatility. Finally, our results indicate the ARCH-jump specification outperforms the ARCH specification in the out-of-sample, one-step-ahead forecast of credit spreads

Surprising impact of random impurities on the anomalous Hall effect in chiral superconductors

The anomalous Hall effect and the closely related polar Kerr effect are arguably the most direct evidence of chiral superconducting pairing. However, disorder or multiband pairing is typically needed for these effects to manifest, as the anomalous Hall conductivity vanishes in clean single-band chiral superconductors. Bearing in mind the candidate chiral superconductor Sr$_2$RuO$_4$, we study the impact of disorder on the Hall response, through real-space simulations of both single-band and two-band chiral superconductors with random impurities. In single-band calculations, we highlight some important differences from the conclusions obtained in previous skew-scattering-type diagramatic analyses. In two-band models, the intrinsic Hall conductivity arising from interband optical transitions are, quite unexpectedly, greatly suppressed by impurity scattering. The suppression stems from the restrictive momentum-space distribution of the corresponding current-current correlator. In addition, we check that random impurities do not induce anomalous Hall effect in non-chiral TRSB superconducting states the likes of $s+i d_{x^2-y^2}$ and $d_{x^2-y^2}+i g_{xy(x^2-y^2)}$. The implications for Kerr effect measurements will be briefly remarked upon.Comment: 7 pages, 4 figure