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Amortized meta-learning methods based on pre-training have propelled fields like natural language processing and vision. Transformer-based neural processes and their variants are leading models for probabilistic meta-learning with a tractable objective. Often trained on synthetic data, these models implicitly capture essential latent information in the data-generation process. However, existing methods do not allow users to flexibly inject (condition on) and extract (predict) this probabilistic latent information at runtime, which is key to many tasks. We introduce the Amortized Conditioning Engine (ACE), a new transformer-based meta-learning model that explicitly represents latent variables of interest. ACE affords conditioning on both observed data and interpretable latent variables, the inclusion of priors at runtime, and outputs predictive distributions for discrete and continuous data and latents. We show ACE's modeling flexibility and performance in diverse tasks such as image completion and classification, Bayesian optimization, and simulation-based inference., Comment: 33 pages, 21 figures

The propagation of elastic waves on discrete periodic Euler-Bernoulli mass-beam lattices is characterised by the competition between coupled translational and rotational degrees-of-freedom at the mass-beam junctions. We influence the dynamics of this system by coupling junctions with beyond-nearest-neighbour spatial connections, affording freedom over the locality of dispersion extrema in reciprocal space, facilitating the emergence of interesting dispersion relations. A generalised dispersion relation for an infinite monatomic mass-beam chain, with any integer order combination of non-local spatial connections, is presented. We demonstrate that competing power channels, between mass and rotational inertia, drive the position and existence of zero group velocity modes within the first Brillouin zone., Comment: 8 pages, 5 figures

The Columbia Stellarator eXperiment (CSX), currently being designed at Columbia University, aims to test theoretical predictions related to QA plasma behavior, and to pioneer the construction of an optimized stellarator using three-dimensional, non-insulated high-temperature superconducting (NI-HTS) coils. The magnetic configuration is generated by a combination of two circular planar poloidal field (PF) coils and two 3D-shaped interlinked (IL) coils, with the possibility to add windowpane coils to enhance shaping and experimental flexibility. The PF coils and vacuum vessel are repurposed from the former Columbia Non-Neutral Torus (CNT) experiment, while the IL coils will be custom-wound in-house using NI-HTS tapes. To obtain a plasma shape that meets the physics objectives with a limited number of coils, novel single-stage optimization techniques are employed, optimizing both the plasma and coils concurrently, in particular targeting a tight aspect ratio QA plasma and minimized strain on the HTS tape. Despite the increased complexity due to the expanded degrees of freedom, these methods successfully identify optimized plasma geometries that can be realized by coils meeting engineering specifications. This paper discusses the derivation of the constraints and objectives specific to CSX, and describe how two recently developed single-stage optimization methodologies are applied to the design of CSX. A set of selected configurations for CSX is then described in detail., Comment: 36 pages, 12 figures

Being three-dimensional, stellarators have the advantage that plasma currents are not essential for creating rotational-transform; however, the external current-carrying coils in stellarators can have strong geometrical shaping, which can complicate the construction. Reducing the inter-coil electromagnetic forces acting on strongly shaped 3D coils and the stress on the support structure, while preserving the favorable properties of the magnetic field is a design challenge. In this work, we recognize that the inter-coil ${\boldsymbol{j}} \times {\boldsymbol{B}}$ forces are the gradient of the vacuum magnetic energy, $\displaystyle E := \frac{1}{2\mu_0}\int_{\mathbb{R}^3} \!\!\! B^2 \, dV$. We introduce an objective functional, ${\mathcal{F}}:= \Phi_2 + \omega E$, built on the usual quadratic flux on a prescribed target surface, $\displaystyle \Phi_2 := \frac{1}{2}\int_{\mathcal{S}} ( {\boldsymbol{B}} \cdot {\boldsymbol{n}} )^2 \, dS$, and the vacuum energy, where $\omega$ is a weight penalty. The Euler-Lagrange equation for stationary states is derived, and numerical illustrations are computed using the SIMSOPT code \cite{simsopt}.

The need for improved diagnostic methods in ophthalmology is acute, especially in the less developed regions with limited access to specialists and advanced equipment. Therefore, we introduce VisionUnite, a novel vision-language foundation model for ophthalmology enhanced with clinical knowledge. VisionUnite has been pretrained on an extensive dataset comprising 1.24 million image-text pairs, and further refined using our proposed MMFundus dataset, which includes 296,379 high-quality fundus image-text pairs and 889,137 simulated doctor-patient dialogue instances. Our experiments indicate that VisionUnite outperforms existing generative foundation models such as GPT-4V and Gemini Pro. It also demonstrates diagnostic capabilities comparable to junior ophthalmologists. VisionUnite performs well in various clinical scenarios including open-ended multi-disease diagnosis, clinical explanation, and patient interaction, making it a highly versatile tool for initial ophthalmic disease screening. VisionUnite can also serve as an educational aid for junior ophthalmologists, accelerating their acquisition of knowledge regarding both common and rare ophthalmic conditions. VisionUnite represents a significant advancement in ophthalmology, with broad implications for diagnostics, medical education, and understanding of disease mechanisms.

Our study aims to increase the spatial resolution of high-sensitivity magnetometry based on singlet-transition infrared (IR) absorption using nitrogen-vacancy (NV) centers in diamonds in monolithic cavities, with potential applications in bio-magnetic field detection. We develop a master-equation treatment of optically detected magnetic resonance, incorporating IR light saturation effects. This master equation provides the singlet population, which is then utilized to calculate the reflectivity and ultimately derive the magnetic field sensitivity taking into account photon and spin shot noise. We further show that our model is compatible with experiments of IR-based NV center magnetometry. Through optimization in a high-parameter space, we uncover the potential to achieve sensitivities in the order of sub-pico tesla, even for sub-millimeter scales.

Quantity and price risks are key uncertainties market participants face in electricity markets with increased volatility, for instance, due to high shares of renewables. From day ahead until real-time, there is a large variation in the best available information, leading to price changes that flexible assets, such as battery storage, can exploit economically. This study contributes to understanding how coordinated bidding strategies can enhance multi-market trading and large-scale energy storage integration. Our findings shed light on the complexities arising from interdependencies and the high-dimensional nature of the problem. We show how stochastic dual dynamic programming is a suitable solution technique for such an environment. We include the three markets of the frequency containment reserve, day-ahead, and intraday in stochastic modelling and develop a multi-stage stochastic program. Prices are represented in a multidimensional Markov Chain, following the scheduling of the markets and allowing for time-dependent randomness. Using the example of a battery storage in the German energy sector, we provide valuable insights into the technical aspects of our method and the economic feasibility of battery storage operation. We find that capacity reservation in the frequency containment reserve dominates over the battery's cycling in spot markets at the given resolution on prices in 2022. In an adjusted price environment, we find that coordination can yield an additional value of up to 12.5%.

When making decisions under uncertainty, individuals often deviate from rational behavior, which can be evaluated across three dimensions: risk preference, probability weighting, and loss aversion. Given the widespread use of large language models (LLMs) in decision-making processes, it is crucial to assess whether their behavior aligns with human norms and ethical expectations or exhibits potential biases. Several empirical studies have investigated the rationality and social behavior performance of LLMs, yet their internal decision-making tendencies and capabilities remain inadequately understood. This paper proposes a framework, grounded in behavioral economics, to evaluate the decision-making behaviors of LLMs. Through a multiple-choice-list experiment, we estimate the degree of risk preference, probability weighting, and loss aversion in a context-free setting for three commercial LLMs: ChatGPT-4.0-Turbo, Claude-3-Opus, and Gemini-1.0-pro. Our results reveal that LLMs generally exhibit patterns similar to humans, such as risk aversion and loss aversion, with a tendency to overweight small probabilities. However, there are significant variations in the degree to which these behaviors are expressed across different LLMs. We also explore their behavior when embedded with socio-demographic features, uncovering significant disparities. For instance, when modeled with attributes of sexual minority groups or physical disabilities, Claude-3-Opus displays increased risk aversion, leading to more conservative choices. These findings underscore the need for careful consideration of the ethical implications and potential biases in deploying LLMs in decision-making scenarios. Therefore, this study advocates for developing standards and guidelines to ensure that LLMs operate within ethical boundaries while enhancing their utility in complex decision-making environments., Comment: Jingru Jia and Zehua Yuan have equal contribution

The azimuthal correlation angle, $\Delta\phi$, between the scattered lepton and the leading jet in deep inelastic $e^{\pm}p$ scattering at HERA has been studied using data collected with the ZEUS detector at a centre-of-mass energy of $\sqrt{s} = 318 \;\mathrm{GeV}$, corresponding to an integrated luminosity of $326 \;\mathrm{pb}^{-1}$. A measurement of jet cross sections in the laboratory frame was made in a fiducial region corresponding to photon virtuality $10 \;\mathrm{GeV}^2 < Q^2 < 350 \;\mathrm{GeV}^2$, inelasticity $0.04 < y < 0.7$, outgoing lepton energy $E_e > 10 \;\mathrm{GeV}$, lepton polar angle $140^\circ < \theta_e < 180^\circ$, jet transverse momentum $2.5 \;\mathrm{GeV} < p_\mathrm{T,jet} < 30 \;\mathrm{GeV}$, and jet pseudorapidity $-1.5 < \eta_\mathrm{jet} < 1.8$. Jets were reconstructed using the $k_\mathrm{T}$ algorithm with the radius parameter $R = 1$. The leading jet in an event is defined as the jet that carries the highest $p_\mathrm{T,jet}$. Differential cross sections, $d\sigma/d\Delta\phi$, were measured as a function of the azimuthal correlation angle in various ranges of leading-jet transverse momentum, photon virtuality and jet multiplicity. Perturbative calculations at $\mathcal{O}(\alpha_{s}^2)$ accuracy successfully describe the data within the fiducial region, although a lower level of agreement is observed near $\Delta\phi \rightarrow \pi$ for events with high jet multiplicity, due to limitations of the perturbative approach in describing soft phenomena in QCD. The data are equally well described by Monte Carlo predictions that supplement leading-order matrix elements with parton showering.

Counterspeech -- speech that opposes hate speech -- has gained significant attention recently as a strategy to reduce hate on social media. While previous studies suggest that counterspeech can somewhat reduce hate speech, little is known about its effects on participation in online hate communities, nor which counterspeech tactics reduce harmful behavior. We begin to address these gaps by identifying 25 large hate communities ("subreddits") within Reddit and analyzing the effect of counterspeech on newcomers within these communities. We first construct a new public dataset of carefully annotated counterspeech and non-counterspeech comments within these subreddits. We use this dataset to train a state-of-the-art counterspeech detection model. Next, we use matching to evaluate the causal effects of hostile and non-hostile counterspeech on the engagement of newcomers in hate subreddits. We find that, while non-hostile counterspeech is ineffective at keeping users from fully disengaging from these hate subreddits, a single hostile counterspeech comment substantially reduces both future likelihood of engagement. While offering nuance to the understanding of counterspeech efficacy, these results a) leave unanswered the question of whether hostile counterspeech dissuades newcomers from participation in online hate writ large, or merely drives them into less-moderated and more extreme hate communities, and b) raises ethical considerations about hostile counterspeech, which is both comparatively common and might exacerbate rather than mitigate the net level of antagonism in society. These findings underscore the importance of future work to improve counterspeech tactics and minimize unintended harm., Comment: 19 pages, 11 figures. arXiv admin note: text overlap with arXiv:2303.13641

Training deep neural networks (DNNs) using traditional backpropagation (BP) presents challenges in terms of computational complexity and energy consumption, particularly for on-device learning where computational resources are limited. Various alternatives to BP, including random feedback alignment, forward-forward, and local classifiers, have been explored to address these challenges. These methods have their advantages, but they can encounter difficulties when dealing with intricate visual tasks or demand considerable computational resources. In this paper, we propose a novel Local Learning rule inspired by neural activity Synchronization phenomena (LLS) observed in the brain. LLS utilizes fixed periodic basis vectors to synchronize neuron activity within each layer, enabling efficient training without the need for additional trainable parameters. We demonstrate the effectiveness of LLS and its variations, LLS-M and LLS-MxM, on multiple image classification datasets, achieving accuracy comparable to BP with reduced computational complexity and minimal additional parameters. Specifically, LLS achieves comparable performance with up to $300 \times$ fewer multiply-accumulate (MAC) operations and half the memory requirements of BP. Furthermore, the performance of LLS on the Visual Wake Word (VWW) dataset highlights its suitability for on-device learning tasks, making it a promising candidate for edge hardware implementations., Comment: 12 pages, 4 figures

The current study focuses on experimentally and theoretically improving the characterization of the drop size and drop velocity for like-on-like doublet impinging jets. The experimental measurements were made using phase Doppler anemometry (PDA) at jet Weber numbers We j corresponding to the impact wave regime of impinging jet atomization. A more suitable dynamic range was used for PDA measurements compared to the literature, resulting in more accurate experimental measurements for drop diameters and velocities. There is some disagreement in the literature regarding the ability of linear stability analysis to accurately predict drop diameters in the impact wave regime. This work seeks to provide some clarity. It was discovered that the assumed uniform jet velocity profile was a contributing factor for deviation between diameter predictions based on models in the literature and experimental measurements. Analytical expressions that depend on parameters based on the assumed jet velocity profile are presented in this work. Predictions based on the parabolic and 1/7th power law turbulent profiles were considered and show better agreement with the experimental measurements compared to predictions based on the previous models. Experimental mean drop velocity measurements were compared with predictions from a force balance analysis, and it was observed that the assumed jet velocity profile also influences the predicted velocities, with the turbulent profile agreeing best with the experimental mean velocity. It is concluded that the assumed jet velocity profile has a predominant effect on drop diameter and velocity predictions.

We extend the computations in our prior work to find the cohomology in degree five of a congruence subgroup Gamma of SL_4(Z) with coefficients in Sym^g(K^4), twisted by a nebentype character eta, along with the action of the Hecke algebra. This is the top cuspidal degree. In this paper we take K to be a finite field of large characteristic, as a proxy for the complex numbers. For each Hecke eigenclass found, we produce the unique Galois representation that appears to be attached to it. The computations require modifications to our previous algorithms to accommodate the fact that the coefficients are not one-dimensional., Comment: arXiv admin note: text overlap with arXiv:1806.08707

Quantum emitters, such as the negatively charged nitrogen-vacancy center in diamond, are attractive for quantum technologies such as nano-sensing, quantum information processing, and as a non-classical light source. However, it is still challenging to position individual emitters in photonic structures whilst preserving the spin coherence properties of the defect. In this paper, we investigate single and ensemble waveguide-integrated nitrogen-vacancy centers in diamond fabricated by femtosecond laser writing followed by thermal annealing. Their spin coherence properties are systematically investigated and are shown to be comparable to native nitrogen-vacancy centers in diamond. This method paves the way for the fabrication of coherent spins integrated within photonic devices., Comment: 9 pages, 5 figures

Chetty et al. (2022) say county density of cross-class friendships (referred to here as "adult-bridging capital") has causal impacts on social mobility within the United States. We instead find that social mobility rates are a function of county density of family capital (higher marriage rates and two-person households), community capital (community organizations, religious congregations, and volunteering), and mean student achievement in grades 3-8. Our models use similar multiple regression equations and the same variables employed by Chetty et al. but also include state fixed effects, student achievement, and family, community, schoolbridging (cross-class high school friendships), and political (participation and institutional trust) capital. School-bridging capital is weakly correlated with mobility if adult-bridging is excluded from the model. R-squared barely changes when adult-bridging is incorporated into the model. When it is included, mobility continues to be significantly correlated with the achievement, family, and community variables but not with school-bridging and political ones. We infer that county mobility rates are largely shaped by parental presence, community life, and student achievement. To enhance mobility, public policy needs to enhance the lives of disadvantaged people at home, in school, and in communities, not just the social class of their friendships.

Denote the virtual cohomological dimension of SL_n(Z) by t=n(n-1)/2. Let St denote the Steinberg module of SL_n(Q) tensored with Q. Let Sh_* denote the sharbly resolution of the Steinberg module St. By Borel-Serre duality, the one-dimensional Q-vector space H^0(SL_n(Z), Q) is isomorphic to H_t(SL_n(Z),St). We find an explicit generator of H_t(SL_n(Z),St) in terms of sharbly cycles and cosharbly cocycles. These methods may extend to other degrees of cohomology of SL_n(Z).

The negatively charged nitrogen-vacancy (NV$^{-}$) center in diamond has shown great potential in nanoscale sensing and quantum information processing due to its rich spin physics. An efficient coupling with light, providing strong luminescence, is crucial for realizing these applications. Laser-written waveguides in diamond promote NV$^{-}$ creation and improve their coupling to light but, at the same time, induce strain in the crystal. The induced strain contributes to light guiding but also affects the energy levels of NV$^{-}$ centers. We probe NV$^{-}$ spin states experimentally with the commonly used continuous-wave zero-field optically detected magnetic resonance (ODMR). In our waveguides, the ODMR spectra are shifted, split, and consistently asymmetric, which we attribute to the impact of local strain. To understand these features, we model ensemble ODMR signals in the presence of strain. By fitting the model results to the experimentally collected ODMR data, we determine the strain tensor components at different positions, thus determining the strain profile across the waveguide. This shows that zero-field ODMR spectroscopy can be used as a strain imaging tool. The resulting strain within the waveguide is dominated by a compressive axial component transverse to the waveguide structure, with a smaller contribution from vertical and shear strain components., Comment: 12 pages, 7 figures + Supplementary Information (6 pages, 3 figures)

The Ward et al. (2016) Plos-One paper is an important, heavily-cited paper in the decoupling literature. The authors present evidence of 1990-2015 growth in material and energy consumption and GDP at a world level, and for selected countries. They find only relative decoupling has occurred, leading to their central claim that future absolute decoupling is implausible. However, the authors have made two key errors in their collected data: GDP data is in current prices which includes inflation, and their global material use data is the total mass of fossil energy materials. Strictly, GDP data should be in constant prices to allow for its comparison over time, and material inputs to an economy should be the sum of mineral raw materials. Amending for these errors, we find much smaller levels of energy-GDP relative decoupling, and no materials-GDP decoupling at all at a global level. We check these new results by adding data for 1900-1990 to provide a longer time series, and find consistently low (and even no) levels of global relative decoupling of material use. The central claim for materials over the implausibility of future absolute decoupling therefore not only remains valid but is reinforced by the corrected datasets., Comment: 6 pages, 4 figures

Rigorous derivations of the approach of individual elements of large isolated systems to a state of thermal equilibrium, starting from arbitrary initial states, are exceedingly rare. This is particularly true for quantum mechanical systems. We demonstrate here how, through a mechanism of repeated scattering, an approach to equilibrium of this type actually occurs in a specific quantum system, one that can be viewed as a natural quantum analog of several previously studied classical models. In particular, we consider an optical mode passing through a reservoir composed of a large number of sequentially-encountered modes of the same frequency, each of which it interacts with through a beam splitter. We then analyze the dependence of the asymptotic state of this mode on the assumed stationary common initial state $\sigma$ of the reservoir modes and on the transmittance $\tau=\cos\lambda$ of the beam splitters. These results allow us to establish that at small $\lambda$ such a mode will, starting from an arbitrary initial system state $\rho$, approach a state of thermal equilibrium even when the reservoir modes are not themselves initially thermalized. We show in addition that, when the initial states are pure, the asymptotic state of the optical mode is maximally entangled with the reservoir and exhibits less nonclassicality than the state of the reservoir modes.

A quantum-limited amplifier enables the amplification of weak signals while introducing minimal noise dictated by the principles of quantum mechanics. These amplifiers serve a broad spectrum of applications in quantum computing, including fast and accurate readout of superconducting qubits and spins, as well as various uses in quantum sensing and metrology. Parametric amplification, primarily developed using Josephson junctions, has evolved into the leading technology for highly effective microwave measurements within quantum circuits. Despite their significant contributions, these amplifiers face fundamental limitations, such as their inability to handle high powers, sensitivity to parasitic magnetic fields, and particularly their limitation to operate only at millikelvin temperatures. To tackle these challenges, here we experimentally develop a novel quantum-limited amplifier based on superconducting kinetic inductance and present an extensive theoretical model to describe this nonlinear coupled-mode system. Our device surpasses the conventional constraints associated with Josephson junction amplifiers by operating at much higher temperatures up to 4.5 K. With two distinct spectral modes and tunability through bias current, this amplifier can operate selectively in both single and double-mode amplification regimes near the quantum noise limit. Utilizing a nonlinear thin film exhibiting kinetic inductance, our device attains gain exceeding 50 dB in a single-mode and 32 dB in a double-mode configuration while adding 0.35 input-referred quanta of noise. Importantly, this amplifier eliminates the need for Josephson junctions, resulting in significantly higher power handling capabilities than Josephson-based amplifiers. It also demonstrates resilience in the presence of magnetic fields, offers a straightforward design, and enhances reliability.

Models are often employed to integrate knowledge about epidemics across scales and simulate disease dynamics. While these approaches have played a central role in studying the mechanics underlying epidemics, we lack ways to reliably predict how the relationship between virulence (the harm to hosts caused by an infection) and transmission will evolve in certain virus-host contexts. In this study, we invoke evolutionary invasion analysis -- a method used to identify the evolution of uninvadable strategies in dynamical systems -- to examine how the virulence-transmission dichotomy can evolve in models of virus infections defined by different natural histories. We reveal that peculiar ecologies drive different evolved relationships between virulence and transmission. Specifically, we discover patterns of virulence evolution between epidemics of various kinds (SARS-CoV-2 and hepatitis C virus) and that varying definitions of virulence alter our predictions for how viruses will evolve. We discuss the findings in light of contemporary conversations in the public health sector around the possibility of predicting virus evolution and in more extensive theoretical discussions involving virulence evolution in emerging infectious diseases.

Fiber-coupled microdisks are a promising platform for enhancing the spontaneous emission from color centers in diamond. The measured cavity-enhanced emission from the microdisk is governed by the effective volume ($V$) of each cavity mode, the cavity quality factor ($Q$), and the coupling between the microdisk and the fiber. Here we observe photoluminescence from an ensemble of nitrogen-vacancy centers into high $Q/V$ microdisk modes, which when combined with coherent spectroscopy of the microdisk modes, allows us to elucidate the relative contributions of these factors. The broad emission spectrum acts as an internal light source facilitating mode identification over several cavity free spectral ranges. Analysis of the fiber-taper collected microdisk emission reveals spectral filtering both by the cavity and the fiber-taper, the latter of which we find preferentially couples to higher-order microdisk modes. Coherent mode spectroscopy is used to measure $Q\sim 1\times10^{5}$ -- the highest reported values for diamond microcavities operating at visible wavelengths. With realistic optimization of the microdisk dimensions, we predict that Purcell factors of $\sim 50$ are within reach., Comment: 15 pages, 4 figures

A new measurement of inclusive-jet cross sections in the Breit frame in neutral current deep inelastic scattering using the ZEUS detector at the HERA collider is presented. The data were taken in the years 2004 to 2007 at a centre-of-mass energy of $318\,\text{GeV}$ and correspond to an integrated luminosity of $347\,\text{pb}^{-1}$. Massless jets, reconstructed using the $k_t$-algorithm in the Breit reference frame, have been measured as a function of the squared momentum transfer, $Q^2$, and the transverse momentum of the jets in the Breit frame, $p_{\perp,\text{Breit}}$. The measured jet cross sections are compared to previous measurements and to perturbative QCD predictions. The measurement has been used in a next-to-next-to-leading-order QCD analysis to perform a simultaneous determination of parton distribution functions of the proton and the strong coupling, resulting in a value of $\alpha_s(M_Z^2) = 0.1142 \pm 0.0017~\text{(experimental/fit)}$ ${}^{+0.0006}_{-0.0007}~\text{(model/parameterisation)}$ ${}^{+0.0006}_{-0.0004}~\text{(scale)}$, whose accuracy is improved compared to similar measurements. In addition, the running of the strong coupling is demonstrated using data obtained at different scales., Comment: 42 pages, 10 figures