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We experimentally study the dispersion relation of waves in a two-dimensional (2D) defect layer with periodic nanopores that sits on a three-dimensional (3D) photonic band gap crystal made from silicon by CMOS-compatible methods. The nanostructures are probed by momentum-resolved broadband near-infrared imaging of p-polarized reflected light that is collected inside the light cone as a function of off-axis wave vectors. We identify surface defect modes at frequencies inside the band gap with a narrow relative linewidth ($\Delta\omega/\omega$ = 0.028), which are absent in defect-free 3D crystals. We calculate the dispersion of modes with relevant mode symmetries using a plane-wave-expansion supercell method with no free parameters. The calculated dispersion matches very well with the measured data. The dispersion is negative in one of the off-axis directions, corresponding to backward-propagating waves where the phase velocity and the group velocity point in opposite directions, as confirmed by finite-difference time-domain simulations. We also present an analytic model of a 2D grating sandwiched between vacuum and a negative real $\epsilon'$ < 0 that mimics the 3D photonic band gap. The model's dispersion agrees with the experiments and with the fuller theory and shows that the backward propagation is caused by the surface grating. We discuss possible applications, including a device that senses the output direction of photons emitted by quantum emitters in response to their frequency.

In contrast to conventional artificial neural networks, which are large and structurally static, we study feed-forward neural networks that are small and dynamic, whose nodes can be added (or subtracted) during training. A single neuronal weight in the network controls the network's size, while the weight itself is optimized by the same gradient-descent algorithm that optimizes the network's other weights and biases, but with a size-dependent objective or loss function. We train and evaluate such Nimble Neural Networks on nonlinear regression and classification tasks where they outperform the corresponding static networks. Growing networks to minimal, appropriate, or optimal sizes while training elucidates network dynamics and contrasts with pruning large networks after training but before deployment., Comment: 8 pages, 7 figures

Ultrasound driven cavitation is widely used to intensify lab and industrial-scale processes. Various studies and experiments demonstrate that the acoustic energy, dissipated through the bubbles collapse, leads to intense physicochemical effects in the processed liquid. A better understanding of these phenomena is crucial for the optimization of ultrasonic reactors, and their scale-up. In the current literature, the visual characterization of the reactor reactivity is mainly carried out with sonoluminescence and sonochemiluminescence. These techniques have limitations in the time resolution since a high camera exposure time is required. In this research, we proposed an alternative method, based on coumarin dosimetry to monitor the hydroxylation activity, and high-speed imaging for the visualization of the vapor field. By this approach, we aim to capture the structure and the dynamics of the vapor field and to correlate this with the chemical effects induced in the ultrasonic reactor. This characterization was carried out for four different ultrasonic probe diameters (3, 7, 14 and 40 mm), displacement amplitudes and processing volumes. Key findings indicate that the probe diameter strongly affects the structure of the vapor field and the chemical effectiveness of the system. The proposed methodology could be applied to characterize other types of ultrasonic reactors with different operating and processing conditions.

We develop representations for bicomplex-valued functions in Hardy classes that generalize the complex holomorphic Hardy spaces. Using these representations, we show these functions have boundary values in the sense of distributions that are representable by an atomic decomposition, and we show continuity of the Hilbert transform on this class of distributional boundary values., Comment: Comments welcome!

Nb3Sn conductors are important candidates for high-field magnets for particle accelerators, and they continue to be widely used for many laboratory and NMR magnets. However, the critical current density, Jc, of present Nb3Sn conductors declines swiftly above 12-15T. State-of-the-art Ta- and Ti-doped strands exhibit upper critical field, Hc2, values of 24-26.5T (4.2K) and do not reach the FCC target Jc, which serves as the present stretch target for Nb3Sn development. As recently demonstrated, to meet this goal requires enhanced vortex pinning but an independent and supplementary approach is to significantly enhance Hc2. In this study, we have arc-melted multiple Nb alloys with added Hf, Zr, Ta and Ti and drawn them successfully into monofilament wires to investigate the possibilities of Hc2 enhancement through alloying. Hc2(T) was measured for all samples in fields up to 16T and some up to 31T. We have found that all alloys show good agreement with the standard Werthamer, Helfand, and Hohenberg (WHH) fitting procedure without the need to adjust the paramagnetic limitation parameter (alpha) and spin-orbit scattering parameter (lambda_so). The evaluation of dHc2/dT near Tc, which is proportional to the electronic specific heat coefficient (gamma) and the normal state resistivity (rho_n), allows a better understanding of the induced disorder introduced by alloying in the A15 phase. So far, we have observed that Hf alloying of pure Nb can enhance Hc2(0) by 3-4T to 28T, while adding just 1 at. %Hf or Zr into a Nb4Ta base alloy can raise Hc2(0) to 31T. Very importantly we find that Hf and Zr raise the alloy recrystallization temperature above the usual A15 reaction temperature range of 650{\deg}C-750{\deg}C, thus ensuring denser A15 phase nucleation in the Nb alloy grain boundaries, possibly leading to a more homogeneous A15 phase Sn content and refined A15 grain size. The potential for..., Comment: 16 pages, 11 figures

Given a budget on total model size, one must decide whether to train a single, large neural network or to combine the predictions of many smaller networks. We study this trade-off for ensembles of random-feature ridge regression models. We prove that when a fixed number of trainable parameters are partitioned among $K$ independently trained models, $K=1$ achieves optimal performance, provided the ridge parameter is optimally tuned. We then derive scaling laws which describe how the test risk of an ensemble of regression models decays with its total size. We identify conditions on the kernel and task eigenstructure under which ensembles can achieve near-optimal scaling laws. Training ensembles of deep convolutional neural networks on CIFAR-10 and a transformer architecture on C4, we find that a single large network outperforms any ensemble of networks with the same total number of parameters, provided the weight decay and feature-learning strength are tuned to their optimal values.

In the months leading up to political elections in the United States, forecasts are widespread and take on multiple forms, including projections of what party will win the popular vote, state ratings, and predictions of vote margins at the state level. It can be challenging to evaluate how accuracy changes in the lead up to Election Day or to put probabilistic forecasts into historical context. Moreover, forecasts differ between analysts, highlighting the many choices in the forecasting process. With this as motivation, here we take a more comprehensive view and begin to unpack some of the choices involved in election forecasting. Building on a prior compartmental model of election dynamics, we present the forecasts of this model across months, years, and types of race. By gathering together monthly forecasts of presidential, senatorial, and gubernatorial races from 2004--2022, we provide a larger-scale perspective and discuss how treating polling data in different ways affects forecast accuracy. We conclude with our 2024 election forecasts (upcoming at the time of writing)., Comment: For our 2024 forecasts, see: https://c-r-u-d.gitlab.io/2024/. Our code is available at: https://gitlab.com/alexandriavolkening/forecasting-elections-using-compartmental-models-2

The Quantum-Enhanced Space-Time (QUEST) experiment consists of a pair of co-located Power Recycled Michelson Interferometers, each designed to have a broadband, shot-noise limited displacement sensitivity of $2\times10^{-19}$ $\mathrm{m/\sqrt{Hz}}$ from 1 to 200 MHz. Here we present the first results of QUEST that set new upper limits on correlated length fluctuations from 13 to 80 MHz, constituting the first broadband constraints for a stochastic gravitational wave background at these frequencies. In a coincident observing run of $10^{4}$ s the averaging of the cross-correlation spectra between the two interferometer signals resulted in a strain sensitivity of $3\times10^{-20}$ $\mathrm{1/\sqrt{Hz}}$ at 40 MHz, making QUEST the most sensitive table-top interferometric system to date.

Solution-based soft matter self-assembly (SA) promises unique materials properties from approaches including additive manufacturing/three-dimensional (3D) printing. We report direct ink writing derived, hierarchically porous transition metal nitride superconductors (SCs) and precursor oxides, structure-directed by Pluronics-family block copolymer (BCP) SA and heat treated in various environments. SCs with periodic lattices on three length scales show record nanoconfinement-induced upper critical field enhancements correlated with BCP molar mass, attaining values of 50 T for NbN and 8.1 T for non-optimized TiN samples, the first mapping of a tailorable SC property onto a macromolecular parameter. They reach surface areas above 120 m$^2$/g, the highest reported for compound SCs to date. Embedded printing enables the first BCP directed mesoporous non-self-supporting helical SCs. Results suggest that additive manufacturing may open pathways to mesoporous SCs with not only a variety of macroscopic form factors but enhanced properties from intrinsic, SA-derived mesostructures with substantial academic and technological promise.

We make use of model-independent statistical methods to assess the consistency of three different supernova compilations: Union3, Pantheon+ and DES 5-year supernovae. We expand the available model space of each, using Crossing Statistics, and test the compatibility of each dataset, against the other two. This is done using (I) a Flat $\Lambda$CDM fitting to, and (II) Iterative Smoothing from, one particular dataset, and determining the level of deformation by required to fit the other two. This allows us to test the mutual consistency of the datasets both within the standard model and in the case of some extended model, motivated by features present in a particular dataset. We find that, in both these cases, the data are only consistent with the point in the parameter space corresponding to zero deformation, at around a $2\sigma$ level, with the DES compilation showing the largest disagreement. However, all three datasets are still found to be consistent to within $1-2\sigma$ for some subset of the extended model space implied by the deformations., Comment: 36 pages, 15 figures

This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations. It highlights the necessity of a collaborative effort to create, establish, and sustain a database that allows findability, accessibility, interoperability, and reusability of molecular dynamics simulation data. Such a development would democratize the field and significantly improve the impact of MD simulations on life science research. This will transform our working paradigm, pushing the field to a new frontier. We invite you to support our initiative at the MDDB community (https://mddbr.eu/community/)

Room temperature cavity quantum electrodynamics with molecular materials in optical cavities offers exciting prospects for controlling electronic, nuclear and photonic degrees of freedom for applications in physics, chemistry and materials science. However, achieving strong coupling with molecular ensembles typically requires high molecular densities and substantial electromagnetic field confinement. These conditions usually involve a significant degree of molecular disorder and a highly structured photonic density of states. It remains unclear to what extent these additional complexities modify the usual physical picture of strong coupling developed for atoms and inorganic semiconductors. Using a microscopic quantum description of molecular ensembles in realistic multimode optical resonators, we show that the emergence of a vacuum Rabi splitting in linear spectroscopy is a necessary but not sufficient metric of coherent admixing between light and matter. In low finesse multi-mode situations we find that molecular dipoles can be partially hybridised with photonic dissipation channels associated with off-resonant cavity modes. These vacuum-induced dissipative processes ultimately limit the extent of light-matter coherence that the system can sustain., Comment: 13 pages, 4 figures

Clifford Taubes showed that the moduli space of the variational equation of the Yang-Mills-Higgs functional on the plane is non-empty, and its elements correspond to "vortices". Inspired by this result, in this paper, we show that the moduli space of the Hitchin-type dimensional reduction of the Seiberg-Witten equations on the plane contains both exponentially decayed solutions and polynomial growth solutions. Furthermore, we show that there is correspondence from the moduli space of exponentially decayed and polynomial growth solutions to the symmetric products of complex numbers. The correspondence restricted to the latter is a surjective map., Comment: 32 pages, comments are welcome!

In-context learning (ICL), the remarkable ability to solve a task from only input exemplars, is often assumed to be a unique hallmark of Transformer models. By examining commonly employed synthetic ICL tasks, we demonstrate that multi-layer perceptrons (MLPs) can also learn in-context. Moreover, MLPs, and the closely related MLP-Mixer models, learn in-context competitively with Transformers given the same compute budget in this setting. We further show that MLPs outperform Transformers on a series of classical tasks from psychology designed to test relational reasoning, which are closely related to in-context classification. These results underscore a need for studying in-context learning beyond attention-based architectures, while also challenging strong prior arguments about MLPs' limited ability to solve relational tasks. Altogether, our results highlight the unexpected competence of MLPs, and support the growing interest in all-MLP alternatives to task-specific architectures., Comment: 30 pages, 10 figures, code available at https://github.com/wtong98/mlp-icl

Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH.

Strong coupling between light and molecular matter is currently attracting interest both in chemistry and physics, in the fast-growing field of molecular polaritonics. The large near-field enhancement of the electric field of plasmonic surfaces and their high tunability make arrays of metallic nanoparticles an interesting platform to achieve and control strong coupling. Two dimensional plasmonic arrays with several nanoparticles per unit cell and crystalline symmetries can host topological edge and corner states. Here we explore the coupling of molecular materials to these edge states using a coupled-dipole framework including long-range interactions. We study both the weak and strong coupling regimes and demonstrate that coupling to topological edge states can be employed to enhance highly-directional long-range energy transfer between molecules., Comment: 26 pages, 4 figures

We provide a simple method that enables readily acquired experimental data to be used to predict whether or not a candidate molecular material may exhibit strong coupling. Specifically, we explore the relationship between the hybrid molecular/photonic (polaritonic) states and the bulk optical response of the molecular material. For a given material this approach enables a prediction of the maximum extent of strong coupling (vacuum Rabi splitting), irrespective of the nature of the confined light field. We provide formulae for the upper limit of the splitting in terms of the molar absorption coefficient, the attenuation coefficient, the extinction coefficient (imaginary part of the refractive index) and the absorbance. To illustrate this approach we provide a number of examples, we also discuss some of the limitations of our approach.

Survival is a key life history parameter that can inform management decisions and life history research. Because true survival is often confounded with permanent and temporary emigration from the study area, many studies must estimate apparent survival (i.e., probability of surviving and remaining inside the study area), which can be much lower than true survival for highly mobile species. One method for estimating true survival is the Barker joint live-recapture/live-resight (JLRLR) model, which combines capture data from a study area (hereafter the capture site) with resighting data from a broader geographic area. This model assumes that live resights occur throughout the entire area where animals can disperse to and this assumption is often not met in practice. Here we use simulation to evaluate survival bias from a JLRLR model under study design scenarios that differ in the site selection for resights: global, random, fixed including the capture site, and fixed excluding the capture site. Simulation results indicate that fixed designs that included the capture site showed negative survival bias, whereas fixed designs that excluded the capture site exhibited positive survival bias. The magnitude of the bias was dependent on movement and survival, where scenarios with high survival and frequent movement had minimal bias. In effort to help minimize bias, we developed a multistate version of the JLRLR and demonstrated reductions in survival bias compared to the single-state version for most designs. Our results suggest minimizing bias can be accomplished by: 1) using a random resight design when feasible and global sampling is not possible, 2) using the multistate JLRLR model when appropriate, 3) including the capture site in the resight sampling frame when possible, and 4) reporting survival as apparent survival if fixed sites are used for resight with the single state JLRLR model., Comment: 30 pages (w/o Appendix A), 8 figures

This paper focuses on modelling loss reserving to pay outstanding claims. As the amount liable on any given claim is not known until settlement, we propose a flexible model via heavy-tailed and skewed distributions to deal with outstanding liabilities. The inference relies on Markov chain Monte Carlo via Gibbs sampler with adaptive Metropolis algorithm steps allowing for fast computations and providing efficient algorithms. An illustrative example emulates a typical dataset based on a runoff triangle and investigates the properties of the proposed models. Also, a case study is considered and shows that the proposed model outperforms the usual loss reserving models well established in the literature in the presence of skewness and heavy tails.