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Edge devices are being deployed at increasing volumes to sense and act on information from the physical world. The discrete Fourier transform (DFT) is often necessary to make this sensed data suitable for further processing $\unicode{x2013}$ such as by artificial intelligence (AI) algorithms $\unicode{x2013}$ and for transmission over communication networks. Analog in-memory computing has been shown to be a fast and energy-efficient solution for processing edge AI workloads, but not for Fourier transforms. This is because of the existence of the fast Fourier transform (FFT) algorithm, which enormously reduces the complexity of the DFT but has so far belonged only to digital processors. Here, we show that the FFT can be mapped to analog in-memory computing systems, enabling them to efficiently scale to arbitrarily large Fourier transforms without requiring large sizes or large numbers of non-volatile memory arrays. We experimentally demonstrate analog FFTs on 1D audio and 2D image signals, using a large-scale charge-trapping memory array with precisely tunable, low-conductance analog states. The scalability of both the new analog FFT approach and the charge-trapping memory device is leveraged to compute a 65,536-point analog DFT, a scale that is otherwise inaccessible by analog systems and which is $>$1000$\times$ larger than any previous analog DFT demonstration. The analog FFT also provides more numerically precise DFTs with greater tolerance to device and circuit non-idealities than a direct matrix-vector multiplication approach. We show that the extension of the FFT algorithm to analog in-memory processors leads to design considerations that differ markedly from digital implementations, and that analog Fourier transforms have a substantial power efficiency advantage at all size scales over FFTs implemented on state-of-the-art digital hardware.

We introduce a novel all-optical platform in multimode and multicore fibres. By using a low-power probe beam and a high-power counter-propagating control beam, we achieve advanced and dynamic control over light propagation within the fibres. This setup enables all-optical reconfiguration of the probe, which is achieved by solely tuning the control beam power. Key operations such as fully tuneable power splitting and mode conversion, core-to-core switching and combination, along with remote probe characterization, are demonstrated at the sub-nanosecond time scale. Our experimental results are supported by a theoretical model that extends to fibres with an arbitrary number of modes and cores. The implementation of these operations in a single platform underlines its versatility, a critical feature of next-generation photonic systems. These results represent a significant shift from existing methods that rely on electro-optical or thermo-optical modulation for tunability. They pave the way towards a fast and energy-efficient alternative through all-optical modulation, a keystone for the advancement of future reconfigurable optical networks and optical computing. Scaling these techniques to highly nonlinear materials could underpin ultrafast all-optically programmable integrated photonics.

In this work, we study a new hollow-core (air-filled) double-clad anti-resonant fiber (DC-ARF) as a potent candidate for multiphoton micro-endoscopy. We compare the fiber characteristics with a single-clad anti-resonant fiber (SC-ARF) and a solid core fiber (SCF). While the DC-ARF and the SC-ARF enable low-loss (<0.2 dBm-1), close to dispersion-free excitation pulse delivery (<10% pulse width increase at 900 nm per 1 m fiber) without any induced non-linearities, the SCF resulted in spectral broadening and pulse-stretching (> 2000% of pulse width increase at 900 nm per 1 m fiber). An ideal optical fiber endoscope needs to be several meters long and should enable both excitation and collection through the fiber. Therefore, we performed multiphoton imaging on endoscopy-compatible 1 m and 3 m lengths of fiber in the back-scattered geometry, wherein the signals were collected either directly (non-descanned detection) or through the fiber (descanned detection). Second harmonic images were collected from barium titanate crystals as well as from biological samples (rat tail tendon). In non-descanned detection conditions, the ARFs outperformed the SCF by up to 10 times in terms of signal-to-noise ratio of images. Significantly, only the DC-ARF, due to its high numerical aperture (0.45) and wide-collection bandwidth (>1 um), could provide images in the de-scanned detection configuration desirable for endoscopy. Thus, our systematic characterization and comparison of different optical fibres under different image collection configurations, confirms and establishes the utility of DC-ARFs for high-performing label-free multiphoton imaging based micro-endoscopy., Comment: 29 pages, 13 figures

Novel fundamental notions helping in the interpretation of the complex dynamics of nonlinear systems are essential to our understanding and ability to exploit them. In this work we predict and demonstrate experimentally a fundamental property of Kerr-nonlinear media, which we name mode rejection and takes place when two intense counter-propagating beams interact in a multimode waveguide. In stark contrast to mode attraction phenomena, mode rejection leads to the selective suppression of a spatial mode in the forward beam, which is controlled via the counter-propagating backward beam. Starting from this observation we generalise the ideas of attraction and rejection in nonlinear multimode systems of arbitrary dimension, which paves the way towards a more general idea of all-optical mode control. These ideas represent universal tools to explore novel dynamics and applications in a variety of optical and non-optical nonlinear systems. Coherent beam combination in polarization-maintaining multicore fibres is demonstrated as example.

Negative control variables are sometimes used in non-experimental studies to detect the presence of confounding by hidden factors. A negative control outcome (NCO) is an outcome that is influenced by unobserved confounders of the exposure effects on the outcome in view, but is not causally impacted by the exposure. Tchetgen Tchetgen (2013) introduced the Control Outcome Calibration Approach (COCA) as a formal NCO counterfactual method to detect and correct for residual confounding bias. For identification, COCA treats the NCO as an error-prone proxy of the treatment-free counterfactual outcome of interest, and involves regressing the NCO on the treatment-free counterfactual, together with a rank-preserving structural model which assumes a constant individual-level causal effect. In this work, we establish nonparametric COCA identification for the average causal effect for the treated, without requiring rank-preservation, therefore accommodating unrestricted effect heterogeneity across units. This nonparametric identification result has important practical implications, as it provides single proxy confounding control, in contrast to recently proposed proximal causal inference, which relies for identification on a pair of confounding proxies. For COCA estimation we propose three separate strategies: (i) an extended propensity score approach, (ii) an outcome bridge function approach, and (iii) a doubly-robust approach. Finally, we illustrate the proposed methods in an application evaluating the causal impact of a Zika virus outbreak on birth rate in Brazil.

Difference-in-differences is undoubtedly one of the most widely used methods for evaluating the causal effect of an intervention in observational (i.e., nonrandomized) settings. The approach is typically used when pre- and post-exposure outcome measurements are available, and one can reasonably assume that the association of the unobserved confounder with the outcome has the same absolute magnitude in the two exposure arms, and is constant over time; a so-called parallel trends assumption. The parallel trends assumption may not be credible in many practical settings, including if the outcome is binary, a count, or polytomous, as well as when an uncontrolled confounder exhibits non-additive effects on the distribution of the outcome, even if such effects are constant over time. We introduce an alternative approach that replaces the parallel trends assumption with an odds ratio equi-confounding assumption under which an association between treatment and the potential outcome under no-treatment is identified with a well-specified generalized linear model relating the pre-exposure outcome and the exposure. Because the proposed method identifies any causal effect that is conceivably identified in the absence of confounding bias, including nonlinear effects such as quantile treatment effects, the approach is aptly called Universal Difference-in-differences (UDiD). Both fully parametric and more robust semiparametric UDiD estimators are described and illustrated in a real-world application concerning the causal effects of a Zika virus outbreak on birth rate in Brazil.

This paper presents the outcomes of a short intercultural exchange project involving pre-service English as a Foreign Language (EFL) teacher training establishments in Sweden and Israel. The project comprised three online meetings recorded in "Zoom" in which student teachers gave feedback on each other's project assignments involving lesson planning and the use of spoken English in the classroom. The sessions were moderated by a highly experienced teacher trainer with contributions from other teacher trainers in the institutions involved. With restrictions imposed on physical meetings and student mobility by the COVID-19 pandemic, the exchange helped to shed light on a number of perennial issues in English language teaching methodology and offers a feasible model for future sustainable virtual exchanges in EFL teacher training. [For the complete volume, "CALL and Professionalisation: Short Papers from EUROCALL 2021 (29th, Online, August 26-27, 2021)," see ED616972.]

We consider studies where multiple measures on an outcome variable are collected over time, but some subjects drop out before the end of follow up. Analyses of such data often proceed under either a 'last observation carried forward' or 'missing at random' assumption. We consider two alternative strategies for identification; the first is closely related to the difference-in-differences methodology in the causal inference literature. The second enables correction for violations of the parallel trend assumption, so long as one has access to a valid 'bespoke instrumental variable'. These are compared with existing approaches, first conceptually and then in an analysis of data from the Framingham Heart Study.

In this paper, we generalize methods in the Difference in Differences (DiD) literature by showing that both additive and multiplicative standard and coarse Structural Nested Mean Models (Robins, 1994, 1997, 1998, 2000, 2004; Lok and Degruttola, 2012; Vansteelandt and Joffe, 2014) are identified under parallel trends assumptions. Our methodology enables adjustment for time-varying covariates, identification of effect heterogeneity as a function of time-varying covariates, identification of additional causal contrasts (such as effects of a `blip' of treatment at a single time point followed by no further treatment and controlled direct effects), and estimation of treatment effects under a general class of treatment patterns (e.g. we do not restrict to the `staggered adoption' setting, and treatments can be multidimensional with any mix of categorical and continuous components). We stress that these extensions come essentially for free, as our parallel trends assumption is not stronger than other parallel trends assumptions in the DiD literature. We also provide a method for sensitivity analysis to violations of our parallel trends assumption. However, in contrast to much of the DiD literature, we only consider panel data, not repeated cross sectional data. We also explain how to estimate optimal treatment regimes via optimal regime Structural Nested Mean Models under parallel trends assumptions plus an additional extremely strong assumption that there is no effect modification by unobserved confounders. Finally, we illustrate our methods with real data applications estimating effects of bank deregulation on housing prices and effects of floods on flood insurance take-up.

Many proposals for the identification of causal effects in the presence of unmeasured confounding require an instrumental variable or negative control that satisfies strong, untestable exclusion restrictions. In this paper, we will instead show how one can identify causal effects for a point exposure by using a measured confounder as a 'bespoke instrumental variable'. This strategy requires an external reference population that does not have access to the exposure, and a stability condition on the confounderoutcome association between reference and target populations. Building on recent identification results of Richardson and Tchetgen Tchetgen (2021), we develop the semiparametric efficiency theory for a general bespoke instrumental variable model, and obtain a multiply robust locally efficient estimator of the average treatment effect in the treated. The utility of the estimators is demonstrated in simulation studies and an analysis of the Life Span Study, concerning atomic bomb survivors in Japan., Comment: 66 pages

The feasibility of coexisting a quantum channel with carrier-grade classical optical channels over Hollow Core Nested Antiresonant Nodeless Fibre (HC-NANF) is experimentally explored for the first time in terms of achievable quantum bit error rate (QBER), secret key rate (SKR) as well as classical signal bit error rates (BER). A coexistence transmission of 1.6 Tbps is achieved for the classical channels simultaneously with a quantum channel over a 2 km-long HC-NANF with a total coexistence power of 0 dBm. To find the best and worst wavelength position for the classical channels, we simulated different classical channels bands with different spacing between the quantum and classical channels considering the crosstalk generated from both Raman scattering and four-wave-mixing (FWM) on the quantum channel. Following our simulation, we numerically estimate the best (Raman spectrum dip) and worst locations (Raman spectrum peak) of the classical channel with respect to its impact on the performance on the quantum channel in terms of SKR and QBER. We further implemented a testbed to experimentally test both single mode fibre (SMF) and HC-NANF in the best and worst-case scenarios. In the best-case scenario, the spacing between quantum and classical is 200 GHz (1.6 nm) with 50 GHz (0.4 nm) spacing between each classical channel. The SKR was preserved without any noticeable changes when coexisting the quantum channel with eight classical channels at 0 dBm total coexistence power in HC-NANF compared to a significant drop of 73% when using SMF at -24 dBm total coexistence power which is 250 times lower than the power used in HC-NANF. In the worst-case scenario using the same powers, and with 1 THz (8 nm) spacing between quantum and classical channels, the SKR dropped 10% using the HC-NANF, whereas in the SMF the SKR plummeted to zero.