Search

A century or less separates the thermonuclear-powered eruptions of recurrent novae in the hydrogen-rich envelopes of massive white dwarfs. The colliding ejecta of successive recurrent nova events are predicted to always generate very large (tens of parsecs) super-remnants; only two examples are currently known. T CrB offers an excellent opportunity to test this prediction. As it will almost certainly undergo its next, once-in ~80-year recurrent nova event between 2024 and 2026, we carried out very deep narrowband and continuum imaging to search for the predicted, piled-up ejecta of the past millenia. While nothing is detected in continuum or narrowband [OIII] images, a ~30-parsec-diameter, faint nebulosity surrounding T CrB is clearly present in deep Halpha, [NII] and [SII] narrowband Condor Array Telescope imagery. We predict that these newly detected nebulosities, as well as the recent ejecta that have not yet reached the super-remnant, are far too optically-thin to capture all but a tiny fraction of the photons emitted by RN flashes. We thus predict that fluorescent light echoes will NOT be detectable following the imminent nova flash of T CrB. Dust may be released by the T CrB red giant wind in pre-eruption outbursts, but we have no reliable estimates of its quantity or geometrical distribution. While we cannot predict the morphology or intensity of dust-induced continuum light echoes following the coming flash, we encourage multi-epoch Hubble Space Telescope optical imaging as well as James Webb Space Telescope infrared imaging of T CrB during the year after it erupts., Comment: 14 pages, 6 Figures, in press in The Astrophysical Journal Letters

We propose two methods to evaluate the conditional Akaike information (cAI) for nonlinear mixed-effects models with no restriction on cluster size. Method 1 is designed for continuous data and includes formulae for the derivatives of fixed and random effects estimators with respect to observations. Method 2, compatible with any type of observation, requires modeling the marginal (or prior) distribution of random effects as a multivariate normal distribution. Simulations show that Method 1 performs well with Gaussian data but struggles with skewed continuous distributions, whereas Method 2 consistently performs well across various distributions, including normal, gamma, negative binomial, and Tweedie, with flexible link functions. Based on our findings, we recommend Method 2 as a distributionally robust cAI criterion for model selection in nonlinear mixed-effects models.

Classical nova eruptions result from thermonuclear-powered runaways in, and ejection of, the hydrogen-rich envelopes of white dwarf stars accreted from their close binary companions. Novae brighten to up to 1,000,000 solar luminosities, and recur thousands of times over their lifetimes spanning several billion years. Between eruptions, mass transfer from the donor star to the white dwarf proceeds via an accretion disk unless the white dwarf possesses a strong magnetic field which can partially or totally disrupt the disk. In that case, accretion is focussed by the white dwarf's magnetic field towards its magnetic poles. Optical spectroscopy and interferometric radio maps demonstrate the presence of bipolar jets, typically arcsec in angular size, and orders of magnitude smaller than one parsec in linear size, during the days to months after nova eruptions. These jets expel collimated matter from the white dwarfs in nova binary stars, but well-resolved images of them are lacking. Here we report the Condor telescope's detection of a hitherto unknown, highly resolved and braided jet, three degrees (at least 25 parsecs) in length. The jet originates at the white dwarf of the old nova GK Persei (nova Per 1901 CE). It precesses on a ~ 3600 yr timescale, and must be at least 7200 years old. Detected across four decades of wavelength, the jet's ultimate energy source is likely the strong accretion shocks near the white dwarf's magnetic poles., Comment: 18 pages, 4 figures

Purpose: To present a novel generalized MR image reconstruction based on pseudoinversion of the encoding matrix (Pinv-Recon) as a simple yet powerful method, and demonstrate its computational feasibility for diverse MR imaging applications. Methods: MR image encoding constitutes a linear mapping of the unknown image to the measured k-space data mediated via an encoding matrix ($ data = Encode \times image$). Pinv-Recon addresses MR image reconstruction as a linear inverse problem ($image = Encode^{-1} \times data$), explicitly calculating the Moore-Penrose pseudoinverse of the encoding matrix using truncated singular value decomposition (tSVD). Using a discretized, algebraic notation, we demonstrate constructing a generalized encoding matrix by stacking relevant encoding mechanisms (e.g., gradient encoding, coil sensitivity encoding, chemical shift inversion) and encoding distortions (e.g., off-center positioning, B$_0$ inhomogeneity, spatiotemporal gradient imperfections, transient relaxation effects). Iterative reconstructions using the explicit generalized encoding matrix, and the computation of the spatial-response-function (SRF) and noise amplification, were demonstrated. Results: We evaluated the computation times and memory requirements (time ~ (size of the encoding matrix)$^{1.4}$). Using the Shepp-Logan phantom, we demonstrated the versatility of the method for various intertwined MR image encoding and distortion mechanisms, achieving better MSE, PSNR and SSIM metrics than conventional methods. A diversity of datasets, including the ISMRM CG-SENSE challenge, were used to validate Pinv-Recon. Conclusion: Although pseudo-inversion of large encoding matrices was once deemed computationally intractable, recent advances make Pinv-Recon feasible. It has great promise for both research and clinical applications, and for educational use., Comment: 26 pages, 8 figures, 2 tables (+ Supplementary Material). Submitted to Magnetic Resonance in Medicine

Recent works show that assembling multiple off-the-shelf large language models (LLMs) can harness their complementary abilities. To achieve this, routing is a promising method, which learns a router to select the most suitable LLM for each query. However, existing routing models are ineffective when multiple LLMs perform well for a query. To address this problem, in this paper, we propose a method called query-based Router by Dual Contrastive learning (RouterDC). The RouterDC model consists of an encoder and LLM embeddings, and we propose two contrastive learning losses to train the RouterDC model. Experimental results show that RouterDC is effective in assembling LLMs and largely outperforms individual top-performing LLMs as well as existing routing methods on both in-distribution (+2.76\%) and out-of-distribution (+1.90\%) tasks. Source code is available at https://github.com/shuhao02/RouterDC., Comment: Accepted by NeurIPS 2024

GPT-4o, an omni-modal model that enables vocal conversations with diverse emotions and tones, marks a milestone for omni-modal foundation models. However, empowering Large Language Models to perceive and generate images, texts, and speeches end-to-end with publicly available data remains challenging in the open-source community. Existing vision-language models rely on external tools for the speech processing, while speech-language models still suffer from limited or even without vision-understanding abilities. To address this gap, we propose EMOVA (EMotionally Omni-present Voice Assistant), to enable Large Language Models with end-to-end speech capabilities while maintaining the leading vision-language performance. With a semantic-acoustic disentangled speech tokenizer, we notice surprisingly that omni-modal alignment can further enhance vision-language and speech abilities compared with the corresponding bi-modal aligned counterparts. Moreover, a lightweight style module is proposed for flexible speech style controls (e.g., emotions and pitches). For the first time, EMOVA achieves state-of-the-art performance on both the vision-language and speech benchmarks, and meanwhile, supporting omni-modal spoken dialogue with vivid emotions., Comment: Project Page: https://emova-ollm.github.io/

Oblivious transfer has been the interest of study as it can be used as a building block for multiparty computation. There are many forms of oblivious transfer; we explore a variant known as Rabin oblivious transfer. Here the sender Alice has one bit, and the receiver Bob obtains this bit with a certain probability. The sender does not know whether the receiver obtained the bit or not. For a previously suggested protocol, we show a possible attack using a delayed measurement. This allows a cheating party to pass tests carried out by the other party, while gaining more information than if they would have been honest. We show how this attack allows perfect cheating, unless the protocol is modified, and suggest changes which lower the cheating probability for the examined cheating strategies., Comment: 8 pages, 1 figure

Jupiter's moon Io is a highly compelling target for future exploration that offers critical insight into tidal dissipation processes and the geology of high heat flux worlds, including primitive planetary bodies, such as the early Earth, that are shaped by enhanced rates of volcanism. Io is also important for understanding the development of volcanogenic atmospheres and mass-exchange within the Jupiter System. However, fundamental questions remain about the state of Io's interior, surface, and atmosphere, as well as its role in the evolution of the Galilean satellites. The Io Volcano Observer (IVO) would address these questions by achieving the following three key goals: (A) Determine how and where tidal heat is generated inside Io; (B) Understand how tidal heat is transported to the surface of Io; and (C) Understand how Io is evolving. IVO was selected for Phase A study through the NASA Discovery program in 2020 and, in anticipation of a New Frontiers 5 opportunity, an enhanced IVO-NF mission concept was advanced that would increase the Baseline mission from 10 flybys to 20, with an improved radiation design; employ a Ka-band communications to double IVO's total data downlink; add a wide angle camera for color and stereo mapping; add a dust mass spectrometer; and lower the altitude of later flybys to enable new science. This study compares and contrasts the mission architecture, instrument suite, and science objectives for Discovery (IVO) and New Frontiers (IVO-NF) missions to Io, and advocates for continued prioritization of Io as an exploration target for New Frontiers., Comment: Submitted to The Planetary Science Journal for peer-review on 14 August 2024

There is now strong evidence that the superconformal index of holographic gauge theories admits a giant graviton expansion capturing the worldvolume dynamics of spherical D3-branes moving in internal space. While the giant graviton indices may be written as suitable contour integrals, care must be taken in their evaluation, as there can be an ambiguity in choice of integration contour or, equivalently, "pole selection". We resolve this ambiguity by using Murthy's recent expansion of the superconformal index to provide a rigorous underpinning of the evaluation of the supergravity giant graviton indices. In doing so, we directly relate Murthy's giant graviton expansion to the supergravity D3-brane expansion., Comment: 31 pages

Multi-task learning, which optimizes performance across multiple tasks, is inherently a multi-objective optimization problem. Various algorithms are developed to provide discrete trade-off solutions on the Pareto front. Recently, continuous Pareto front approximations using a linear combination of base networks have emerged as a compelling strategy. However, it suffers from scalability issues when the number of tasks is large. To address this issue, we propose a novel approach that integrates a main network with several low-rank matrices to efficiently learn the Pareto manifold. It significantly reduces the number of parameters and facilitates the extraction of shared features. We also introduce orthogonal regularization to further bolster performance. Extensive experimental results demonstrate that the proposed approach outperforms state-of-the-art baselines, especially on datasets with a large number of tasks., Comment: ICML 2024 (Spotlight)

We show that in a spacetime geometry with general connection, there exist field redefinitions that replace the mixed-symmetry nonmetricity of the asymmetric connection with the sum of torsion and the field strength of special conformal transformations of a Lorentzian connection. In contrast to general relativity, generic action functionals give different results for metric versus Palatini-style variations. We consider some of the resulting issues in Poincar\`e gauge theory, where it is natural to vary both the solder form and the spin connection, and where the action may differ from the Einstein-Hilbert case. Independent variation may introduce nonmetricity as well as torsion. Appending the defining equation of nonmetricity to the Maurer-Cartan equations of the Poincar\`e Lie algebra, we show that the mixed symmetry components of nonmetricity can be absorbed into an altered torsion tensor, restoring local Lorentz symmetry and metric compatibility while including quadratic terms in the redefined curvature. In order to recover the original torsion and nonmetric fields, we replace the definition of nonmetricity by an additional structure equation. We show that the maximal Lie algebra compatible with the enlarged set is isomorphic to the conformal Lie algebra. Therefore, in a Lorentzian conformal geometry the sum of torsion and the field strength of special conformal transformations is given by the mixed symmetry nonmetricity of an equivalent asymmetric system., Comment: 11 pages plus citations

High-eccentricity gas giant planets serve as unique laboratories for studying the thermal and chemical properties of H/He-dominated atmospheres. One of the most extreme cases is HD 80606b -- a hot Jupiter orbiting a sun-like star with an eccentricity of $0.93$ -- which experiences an increase in incident flux of nearly three orders of magnitude as the star-planet separation decreases from $0.88\,{\rm au}$ at apoastron to $0.03\,{\rm au}$ at periastron. We observed the planet's periastron passage using \emph{JWST}'s NIRSpec/G395H instrument ($2.8-5.2\,{\rm \mu m}$) during a $21\,{\rm hr}$ window centered on the eclipse. We find that, as the planet passes through periastron, its emission spectrum transitions from a featureless blackbody to one in which CO and CH$_4$ absorption features are visible. We obtain significant detections of CH$_4$ during post-periapse phases at $3.7-4.8\sigma$ depending on the phase. Following periapse, CO and H$_2$O are also detected at $3.4\sigma$ and $3.1\sigma$, respectively. Furthermore, we rule out the presence of a strong temperature inversion near the IR photosphere -- predicted by GCMs to form temporarily during periapse passage -- based on the lack of obvious emission features throughout the observing window. Our study demonstrates the feasibility of studying hot Jupiter atmospheres using partial phase curves obtained with NIRSpec/G395H., Comment: 25 pages, 19 figures, 2 tables. Submitted to ApJ

The central regions of the Milky Way constitute a unique laboratory for a wide swath of astrophysical studies, consequently the inner $\sim$400 pc has been the target of numerous large surveys at all accessible wavelengths. In this paper we present a catalog of sources at 25 and 37 $\mu$m located within all of the regions observed with the SOFIA/FORCAST instrument in the inner $\sim$200 pc of the Galaxy. The majority of the observations were obtained as part of the SOFIA Cycle 7 Galactic Center Legacy program survey, which was designed to complement the Spitzer/MIPS 24 $\mu$m catalog in regions saturated in the MIPS observations. Due to the wide variety of source types captured by our observations at 25 and 37 $\mu$m, we do not limit the FORCAST source catalog to unresolved point sources, or treat all sources as if they are point-like sources. The catalog includes all detectable sources in the regions, resulting in a catalog of 950 sources, including point sources, compact sources, and extended sources. We also provide the user with metrics to discriminate between the source types., Comment: 29 pages, 13 figures, Accepted to ApJ

Pre-training followed by fine-tuning is widely adopted among practitioners. The performance can be improved by "model soups"~\cite{wortsman2022model} via exploring various hyperparameter configurations.The Learned-Soup, a variant of model soups, significantly improves the performance but suffers from substantial memory and time costs due to the requirements of (i) having to load all fine-tuned models simultaneously, and (ii) a large computational graph encompassing all fine-tuned models. In this paper, we propose Memory Efficient Hyperplane Learned Soup (MEHL-Soup) to tackle this issue by formulating the learned soup as a hyperplane optimization problem and introducing block coordinate gradient descent to learn the mixing coefficients. At each iteration, MEHL-Soup only needs to load a few fine-tuned models and build a computational graph with one combined model. We further extend MEHL-Soup to MEHL-Soup+ in a layer-wise manner. Experimental results on various ViT models and data sets show that MEHL-Soup(+) outperforms Learned-Soup(+) in terms of test accuracy, and also reduces memory usage by more than $13\times$. Moreover, MEHL-Soup(+) can be run on a single GPU and achieves $9\times$ speed up in soup construction compared with the Learned-Soup. The code is released at https://github.com/nblt/MEHL-Soup., Comment: ECCV 2024

Long COVID is characterized by persistent symptoms, particularly pulmonary impairment, which necessitates advanced imaging for accurate diagnosis. Hyperpolarised Xenon-129 MRI (XeMRI) offers a promising avenue by visualising lung ventilation, perfusion, as well as gas transfer. Integrating functional data from XeMRI with structural data from Computed Tomography (CT) is crucial for comprehensive analysis and effective treatment strategies in long COVID, requiring precise data alignment from those complementary imaging modalities. To this end, CT-MRI registration is an essential intermediate step, given the significant challenges posed by the direct alignment of CT and Xe-MRI. Therefore, we proposed an end-to-end multimodal deformable image registration method that achieves superior performance for aligning long-COVID lung CT and proton density MRI (pMRI) data. Moreover, our method incorporates a novel Multi-perspective Loss (MPL) function, enhancing state-of-the-art deep learning methods for monomodal registration by making them adaptable for multimodal tasks. The registration results achieve a Dice coefficient score of 0.913, indicating a substantial improvement over the state-of-the-art multimodal image registration techniques. Since the XeMRI and pMRI images are acquired in the same sessions and can be roughly aligned, our results facilitate subsequent registration between XeMRI and CT, thereby potentially enhancing clinical decision-making for long COVID management.

Ignoring ten-dimensional heterotic gauge fields, heterotic supergravity reduced on a $d$-dimensional torus gives rise to a half-maximal supergravity coupled to $d$ vector multiplets. The reduced theory has a continuous $O(d,d;\mathbb R)/O(d)_-\times O(d)_+$ symmetry that persists to all perturbative orders in the string $\alpha'$ expansion. We highlight this symmetry by explicitly reducing the bosonic sector of four-derivative heterotic supergravity as well as its fermionic supersymmetry variations. After appropriate field redefinitions, the resulting action and supersymmetry variations are manifestly $O(d)_-\times O(d)_+$ invariant. This reduction allows us to explore the interplay between the gravity and vector multiplets beyond leading order, where (in our conventions) $O(d)_-$ is the supergravity R-symmetry while $O(d)_+$ is a flavor symmetry of the $d$ vector multiplets., Comment: 26 pages, references added

Distributed learning, which does not require gathering training data in a central location, has become increasingly important in the big-data era. In particular, random-walk-based decentralized algorithms are flexible in that they do not need a central server trusted by all clients and do not require all clients to be active in all iterations. However, existing distributed learning algorithms assume that all learning clients share the same task. In this paper, we consider the more difficult meta-learning setting, in which different clients perform different (but related) tasks with limited training data. To reduce communication cost and allow better privacy protection, we propose LoDMeta (Local Decentralized Meta-learning) with the use of local auxiliary optimization parameters and random perturbations on the model parameter. Theoretical results are provided on both convergence and privacy analysis. Empirical results on a number of few-shot learning data sets demonstrate that LoDMeta has similar meta-learning accuracy as centralized meta-learning algorithms, but does not require gathering data from each client and is able to better protect data privacy for each client.

Mixup and its variants form a popular class of data augmentation techniques.Using a random sample pair, it generates a new sample by linear interpolation of the inputs and labels. However, generating only one single interpolation may limit its augmentation ability. In this paper, we propose a simple yet effective extension called multi-mix, which generates multiple interpolations from a sample pair. With an ordered sequence of generated samples, multi-mix can better guide the training process than standard mixup. Moreover, theoretically, this can also reduce the stochastic gradient variance. Extensive experiments on a number of synthetic and large-scale data sets demonstrate that multi-mix outperforms various mixup variants and non-mixup-based baselines in terms of generalization, robustness, and calibration.

Reinforcement Learning from Human Feedback (RLHF) has been commonly used to align the behaviors of Large Language Models (LLMs) with human preferences. Recently, a popular alternative is Direct Policy Optimization (DPO), which replaces an LLM-based reward model with the policy itself, thus obviating the need for extra memory and training time to learn the reward model. However, DPO does not consider the relative qualities of the positive and negative responses, and can lead to sub-optimal training outcomes. To alleviate this problem, we investigate the use of intrinsic knowledge within the on-the-fly fine-tuning LLM to obtain relative qualities and help to refine the loss function. Specifically, we leverage the knowledge of the LLM to design a refinement function to estimate the quality of both the positive and negative responses. We show that the constructed refinement function can help self-refine the loss function under mild assumptions. The refinement function is integrated into DPO and its variant Identity Policy Optimization (IPO). Experiments across various evaluators indicate that they can improve the performance of the fine-tuned models over DPO and IPO.

Oblivious transfer between two untrusting parties is an important primitive in cryptography. There are different variants of oblivious transfer. In Rabin oblivious transfer, the sender Alice holds a bit, and the receiver Bob either obtains the bit, or obtains no information with probability $p_?$. Alice should not know whether or not Bob obtained the bit. We examine a quantum Rabin oblivious transfer (OT) protocol that uses two pure states. Investigating different cheating scenarios for the sender and for the receiver, we determine optimal cheating probabilities in each case. Comparing the quantum Rabin oblivious transfer protocol to classical Rabin oblivious transfer protocols, we show that the quantum protocol outperforms classical protocols, which do not use a third party, for some values of $p_?$. We find that quantum Rabin OT protocols that use mixed states can outperform quantum Rabin OT protocols that use pure states for some values of $p_?$., Comment: 12 pages, 3 figures; v2: added Appendix C and other minor additions

We present a novel agent-based approach to simulating an over-the-counter (OTC) financial market in which trades are intermediated solely by market makers and agent visibility is constrained to a network topology. Dynamics, such as changes in price, result from agent-level interactions that ubiquitously occur via market maker agents acting as liquidity providers. Two additional agents are considered: trend investors use a deep convolutional neural network paired with a deep Q-learning framework to inform trading decisions by analysing price history; and value investors use a static price-target to determine their trade directions and sizes. We demonstrate that our novel inclusion of a network topology with market makers facilitates explorations into various market structures. First, we present the model and an overview of its mechanics. Second, we validate our findings via comparison to the real-world: we demonstrate a fat-tailed distribution of price changes, auto-correlated volatility, a skew negatively correlated to market maker positioning, predictable price-history patterns and more. Finally, we demonstrate that our network-based model can lend insights into the effect of market-structure on price-action. For example, we show that markets with sparsely connected intermediaries can have a critical point of fragmentation, beyond which the market forms distinct clusters and arbitrage becomes rapidly possible between the prices of different market makers. A discussion is provided on future work that would be beneficial., Comment: 20 pages, 17 figures

The fusion research facility ITER is currently being assembled to demonstrate that fusion can be used for industrial energy production, while several other programmes across the world are also moving forward, such as EU-DEMO, CFETR, SPARC and STEP. The high engineering complexity of a tokamak makes it an extremely challenging device to optimise, and test-based optimisation would be too slow and too costly. Instead, digital design and optimisation must be favored, which requires strongly-coupled suites of High-Performance Computing calculations. In this context, having surrogate models to provide quick estimates with uncertainty quantification is essential to explore and optimise new design options. Furthermore, these surrogates can in turn be used to accelerate simulations in the first place. This is the case of Parareal, a time-parallelisation method that can speed-up large HPC simulations, where the coarse-solver can be replaced by a surrogate. A novel framework, Neural-Parareal, is developed to integrate the training of neural operators dynamically as more data becomes available. For a given input-parameter domain, as more simulations are being run with Parareal, the large amount of data generated by the algorithm is used to train new surrogate models to be used as coarse-solvers for future Parareal simulations, leading to progressively more accurate coarse-solvers, and thus higher speed-up. It is found that such neural network surrogates can be much more effective than traditional coarse-solver in providing a speed-up with Parareal. This study is a demonstration of the convergence of HPC and AI which simply has to become common practice in the world of digital engineering design.

As the capabilities of large language models (LLMs) have expanded dramatically, aligning these models with human values presents a significant challenge. Traditional alignment strategies rely heavily on human intervention, such as Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), or on the self-alignment capacities of LLMs, which usually require a strong LLM's emergent ability to improve its original bad answer. To address these challenges, we propose a novel self-alignment method that utilizes a Chain of Thought (CoT) approach, termed AlignCoT. This method encompasses stages of Question Analysis, Answer Guidance, and Safe Answer production. It is designed to enable LLMs to generate high-quality, safe responses throughout various stages of their development. Furthermore, we introduce the Mixture of insighTful Experts (MoTE) architecture, which applies mixture of experts to enhance each component of the AlignCoT process, markedly increasing alignment efficiency. The MoTE approach not only outperforms existing methods in aligning LLMs with human values but also highlights the benefits of using self-generated data, revealing the dual benefits of improved alignment and training efficiency.

Natural climate solutions (NCS) are critical for mitigating climate change through ecosystem-based carbon removal and emissions reductions. NCS implementation can also generate biodiversity and human well-being co-benefits and trade-offs ("NCS co-impacts"), but the volume of evidence on NCS co-impacts has grown rapidly across disciplines, is poorly understood, and remains to be systematically collated and synthesized. A global evidence map of NCS co-impacts would overcome key barriers to NCS implementation by providing relevant information on co-benefits and trade-offs where carbon mitigation potential alone does not justify NCS projects. We employ large language models to assess over two million articles, finding 257,266 relevant articles on NCS co-impacts. We analyze this large and dispersed body of literature using innovative machine learning methods to extract relevant data (e.g., study location, species, and other key variables), and create a global evidence map on NCS co-impacts. Evidence on NCS co-impacts has grown approximately ten-fold in three decades, although some of the most abundant evidence is associated with pathways that have less mitigation potential. We find that studies often examine multiple NCS pathways, indicating natural NCS pathway complements, and each NCS is often associated with two or more coimpacts. Finally, NCS co-impacts evidence and priority areas for NCS are often mismatched--some countries with high mitigation potential from NCS have few published studies on the broader co-impacts of NCS implementation. Our work advances and makes available novel methods and systematic and representative data of NCS co-impacts studies, thus providing timely insights to inform NCS research and action globally., Comment: 28 pages, 5 figures

Ouzo is a well-known drink in Mediterranean countries, with ingredients water, alcohol and trans-anethole oil. The oil is insoluble in water, but completely soluble in alcohol, so when water is added to the spirit, the available alcohol is depleted and the mixture exhibits spontaneous emulsification. This process is commonly known as the louche or Ouzo effect. Although the phase boundaries of this archetypal ternary mixture are well known, the properties of coexisting phases have not previously been studied. Here, we present a detailed experimental investigation into the phase behaviour, including tie-lines connecting coexisting phases, determination of the critical point (also called the plait point in ternary systems) and measurements of the surface tension and density for varying alcohol concentrations. Additionally, we present a theory for the thermodynamics and phase diagram of the system. With suitable selection of the interaction parameters, the theory captures nearly all features of the experimental work. This simple model can be used to determine both bulk and non-uniform (e.g. interfacial) properties, paving the way for a wide range of future applications of the model to ternary mixtures in general. We show how our accurate equilibrium phase diagram can be used to provide improved understanding of non-equilibrium phenomena., Comment: 15 pages, 9 figures

We revisit the existence of monotonic quantities along renormalization group flows using only the Null Energy Condition and the Ryu-Takayanagi formula for the entanglement entropy of field theories with anti-de Sitter gravity duals. In particular, we consider flows within the same dimension and holographically reprove the $c$-, $F$-, and $a$-theorems in dimensions two, three, and four. We focus on the family of maximally spherical entangling surfaces, define a quasi-constant of motion corresponding to the breaking of conformal invariance, and use a properly defined distance between minimal surfaces to construct a holographic $c$-function that is monotonic along the flow. We then apply our method to the case of flows across dimensions: There, we reprove the monotonicity of flows from $\mathrm{AdS}_{D+1}$ to $\mathrm{AdS}_3$ and prove the novel case of flows from $\mathrm{AdS}_5$ to $\mathrm{AdS}_4$., Comment: 28 pages, 3 figures; references added

Ehlers, Pirani, and Schild argued that measurements of null and timelike geodesics yield Weyl and projective connections, respectively, with compatibility in the lightlike limit giving an integrable Weyl connection. Their conclusions hold only for a 4-dim representation of the conformal connection on the null cone, and by restricting reparameterizations of timelike geodesics to yield a torsion-free, affine connection. An arbitrary connection gives greater freedom. A linear connection for the conformal symmetry of null geodesics requires the SO(4,2) representation. The enlarged class of projective transformations of timelike geodesics changes Weyl's projective curvature, and we find invariant forms of the torsion and nonmetricity, along with a new, invariant, second rank tensor field generalizing the dilatational curvature without requiring a metric. We show that either projective or conformal connections require a monotonic, twice differentiable function on a spacetime region foliated by order isomorphic, totally ordered, twice differentiable timelike curves in a necessarily Lorentzian geometry. We prove that the conditions for projective and conformal Ricci flatness imply each other and gauge choices within either can reduce the geometry to the original Riemannian form. Thus, measurements of null and timelike geodesics lead to an SO(4,2) connection, with no requirement for a lightlike limit. Reduction to integrable Weyl symmetry can only follow from the field equations of a gravity theory. We show that the simplest quadratic spacetime action leads to this reduction., Comment: Reworked for conciseness, to stress new results, and to present stronger claims. 19 pages plus references

We explore the structure and magnetic field response of edge dislocations in Grandjean-Cano wedge cells filled with chiral mixtures of the ferroelectric nematic mesogen DIO. Upon cooling, the ordering changes from paraelectric in the cholesteric phase N* to antiferroelectric in the smectic SmZ_A* and to ferroelectric in the cholesteric N_F*. Dislocations of the Burgers vector b equal the helicoidal pitch P are stable in all three phases, while dislocations with b=P/2 exist only in the N* and SmZ_A*. The b=P/2 dislocations split into pairs of {\tau}(-1/2) {\lambda}(+1/2) disclinations, while the thick dislocations b=P are pairs of nonsingular {\lambda}(-1/2) {\lambda}(+1/2) disclinations. The polar order makes the {\tau}(-1/2) disclinations unstable in the N_F* phase, as they should be connected to singular walls in the polarization field. We propose a model of transformation of the composite {\tau}(-1/2) line-wall defect into a nonsingular {\lambda}(-1/2) disclination, which is paired up with a {\lambda}(+1/2) line to form a b=P dislocation. The SmZ_A* behavior in the in-plane magnetic field is different from that of the N_F* and N*: the dislocations show no zigzag instability, and the pitch remains unchanged in the magnetic fields up to 1 T. The behavior is associated with the finite compressibility of smectic layers., Comment: 28 pages, 10 figures, 1 table

This article presents an electron tunneling noise programmable random variate accelerator for accelerating the sampling stage of Monte Carlo simulations. We used the LiteX framework to generate a FemtoRV imfc RISC-V instruction set soft processor and deploy it on a Digilent Arty-100T FPGA development board. The RISC-V soft processor augmented with our programmable random variate accelerator achieves an average speedup of 8.70 times and a median speedup of 8.68 times for a suite of twelve different benchmark applications when compared to GNU Scientific Library software random number generation. These speedups are achievable because the benchmarks spend an average of 90.0 % of their execution time generating random samples. The results of the Monte Carlo benchmark programs run over the programmable random variate accelerator have an average Wasserstein distance of 1.48 times and a median Wasserstein distance of 1.41 times that of the results produced by the GNU Scientific Library random number generators. The soft processor samples the electron tunneling noise source using the hardened XADC block in the FPGA. The flexibility of the LiteX framework allows for the deployment of any LiteX-supported soft processor with an electron tunneling noise programmable random variate accelerator on any LiteX-supported development board that contains an FPGA with an XADC.