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In this paper we develop homogenization theory for spatiotemporally modulated wire medium. We first solve for the modal waves that are supported by this composite medium, we show peculiar properties such as extraordinary waves that propagate at frequencies below the cut-off frequency of the corresponding stationary medium. We explain how these unique solutions give rise to an extreme Fresnel drag that exists already with weak and slow spatiotemporal modulation. Next, we turn to derive the effective material permittivity that corresponds to each of the first few supported modes, and write the average fields and Poynting's vector. Nonlocality, nonreciprocity, and anisotropy due to the spatiotemporal modulation direction, are three inherent properties of this medium, and are clearly seen in the effective material parameters. As a figure of merit, we also derive the effective permittivity of a plasma medium with spatiotemporally modulated plasma frequency. This comparison is interesting since the plasma medium can be considered as the effective medium that is obtained by a stationary wire medium. We validate that homogenization and spatiotemporal variation are not necessarily interchangeable operations. And indeed, in certain parameter regimes the homogenization should be performed directly on spatiotemporally modulated composite medium, rather than first homogenize the stationary medium and then phenomenologically introduce the effect of the space-time modulation., Comment: 15 pages, 9 figures

It has become increasingly common for data to be collected adaptively, for example using contextual bandits. Historical data of this type can be used to evaluate other treatment assignment policies to guide future innovation or experiments. However, policy evaluation is challenging if the target policy differs from the one used to collect data, and popular estimators, including doubly robust (DR) estimators, can be plagued by bias, excessive variance, or both. In particular, when the pattern of treatment assignment in the collected data looks little like the pattern generated by the policy to be evaluated, the importance weights used in DR estimators explode, leading to excessive variance. In this paper, we improve the DR estimator by adaptively weighting observations to control its variance. We show that a t-statistic based on our improved estimator is asymptotically normal under certain conditions, allowing us to form confidence intervals and test hypotheses. Using synthetic data and public benchmarks, we provide empirical evidence for our estimator's improved accuracy and inferential properties relative to existing alternatives.

In this paper we study the electromagnetic scattering, absorption, and performance bounds for short time modulated pulses that impinge on a time-varying lossy layer that is sandwiched between vacuum and a perfect electric conductor. The electric characteristics of the layer, namely, the conductivity, permittivity, and permeability are assumed to change abruptly or gradually in time. We demonstrate numerically that a time-varying absorbing layer that undergoes temporal switching of its permittivity and conductance can absorb the power of a modulated, ultra-wideband, as well as a quasi-monochromatic, pulsed wave beyond what is dictated by the time invariant Rozanov bound when integrating over the whole frequency spectrum. We suggest and simulate a practical metamaterial realization that is constructed as a three-dimensional array of resistor loaded dipole. By switching only the dipole's load resistance, desired effective media properties are obtained. Furthermore, we show that Rozanov's bound can be bypassed with abrupt and a more practical gradual, soft, switching thus overcoming some possible causality issue in abrupt switching., Comment: 11 pages, 11 figures

Certain applications require the use of signals that combine both the capability to operate with low signal-to-noise ratios and the ability to support multiple users without interference. In the case where many users have very different signal-to-noise ratios, it is necessary to consider coding schemes that can be used in a multi-user environment but with different noise immunity levels. Traditional detection systems based on the correlation function and coding sequences have significant limitations in satisfying both objectives, since the cross-correlation between coded signals corresponding with different users is linked to the use of the same coded sequences length. The research topic of binary sequences that have null cross-correlation and different length has not been studied in depth, but it has potential applications in multi-user environments. In this work an algorithm to generate binary sequences completely uncorrelated with certain sets of complementary sequences is presented. The proposed algorithm is based on nested Barker sequences, and it is compared with a previous proposal based on an iterative algorithm. This approach allows to generate more diversity of sequences of different length than the iterative approach, which it makes useful for applications based on binary sequences detection and expand the horizon of many applications., Comment: 8 pages and 5 figures

The main objective of this paper is to present an algorithm of pricing perpetual American put options with asset-dependent discounting. The value function of such an instrument can be described as \begin{equation*} V^{\omega}_{\text{A}^{\text{Put}}}(s) = \sup_{\tau\in\mathcal{T}} \mathbb{E}_{s}[e^{-\int_0^\tau \omega(S_w) dw} (K-S_\tau)^{+}], \end{equation*} where $\mathcal{T}$ is a family of stopping times, $\omega$ is a discount function and $\mathbb{E}$ is an expectation taken with respect to a martingale measure. Moreover, we assume that the asset price process $S_t$ is a geometric L\'evy process with negative exponential jumps, i.e. $S_t = s e^{\zeta t + \sigma B_t - \sum_{i=1}^{N_t} Y_i}$. The asset-dependent discounting is reflected in the $\omega$ function, so this approach is a generalisation of the classic case when $\omega$ is constant. It turns out that under certain conditions on the $\omega$ function, the value function $V^{\omega}_{\text{A}^{\text{Put}}}(s)$ is convex and can be represented in a closed form; see Al-Hadad and Palmowski (2021). We provide an option pricing algorithm in this scenario and we present exact calculations for the particular choices of $\omega$ such that $V^{\omega}_{\text{A}^{\text{Put}}}(s)$ takes a simplified form.

Computationally efficient contextual bandits are often based on estimating a predictive model of rewards given contexts and arms using past data. However, when the reward model is not well-specified, the bandit algorithm may incur unexpected regret, so recent work has focused on algorithms that are robust to misspecification. We propose a simple family of contextual bandit algorithms that adapt to misspecification error by reverting to a good safe policy when there is evidence that misspecification is causing a regret increase. Our algorithm requires only an offline regression oracle to ensure regret guarantees that gracefully degrade in terms of a measure of the average misspecification level. Compared to prior work, we attain similar regret guarantees, but we do no rely on a master algorithm, and do not require more robust oracles like online or constrained regression oracles (e.g., Foster et al. (2020a); Krishnamurthy et al. (2020)). This allows us to design algorithms for more general function approximation classes., Comment: ICML 2021

Tractable contextual bandit algorithms often rely on the realizability assumption - i.e., that the true expected reward model belongs to a known class, such as linear functions. In this work, we present a tractable bandit algorithm that is not sensitive to the realizability assumption and computationally reduces to solving a constrained regression problem in every epoch. When realizability does not hold, our algorithm ensures the same guarantees on regret achieved by realizability-based algorithms under realizability, up to an additive term that accounts for the misspecification error. This extra term is proportional to T times a function of the mean squared error between the best model in the class and the true model, where T is the total number of time-steps. Our work sheds light on the bias-variance trade-off for tractable contextual bandits. This trade-off is not captured by algorithms that assume realizability, since under this assumption there exists an estimator in the class that attains zero bias., Comment: 35 pages, 6 figures

In this paper we consider the following optimal stopping problem $$V^{\omega}_{\rm A}(s) = \sup_{\tau\in\mathcal{T}} \mathbb{E}_{s}[e^{-\int_0^\tau \omega(S_w) dw} g(S_\tau)],$$ where the process $S_t$ is a jump-diffusion process, $\mathcal{T}$ is a family of stopping times while $g$ and $\omega$ are fixed payoff function and discount function, respectively. In a financial market context, if $g(s)=(K-s)^+$ or $g(s)=(s-K)^+$ and $\mathbb{E}$ is the expectation taken with respect to a martingale measure, $V^{\omega}_{\rm A}(s)$ describes the price of a perpetual American option with a discount rate depending on the value of the asset process $S_t$. If $\omega$ is a constant, the above problem produces the standard case of pricing perpetual American options. In the first part of this paper we find sufficient conditions for the convexity of the value function $V^{\omega}_{\rm A}(s)$. This allows us to determine the stopping region as a certain interval and hence we are able to identify the form of $V^{\omega}_{\rm A}(s)$. We also prove a put-call symmetry for American options with asset-dependent discounting. In the case when $S_t$ is a geometric L\'evy process we give exact expressions using the so-called omega scale functions introduced in Li and Palmowski (2018). We prove that the analysed value function satisfies the HJB equation and we give sufficient conditions for the smooth fit property as well. Finally, we present a few examples for which we obtain the analytical form of the value function $V^{\omega}_{\rm A}(s)$.

The area of a future holographic screen increases monotonically. Associating this area with entropy results in a generalized second law for Cosmology (GSLC). Unlike black hole horizons, screens relevant to Cosmology have no thermodynamical interpretation. We suggest relating the entropy of the screens to spacetime degrees of freedom surface density derived from a (seemingly) phase space. This construction enables us to identify the entropy of any holographic screen as the entropy detected by accelerating observers due to their acceleration. Using Unruh's temperature and the equivalence principle, this gives the holographic screens' temperature and yields a thermodynamical interpretation of the GSLC., Comment: 5 pages, no figures