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Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

In this work, we present a unified theoretical framework that simultaneously addresses some of the most intriguing puzzles in particle physics and cosmology, namely the origins of neutrino mass, baryon asymmetry, and cosmic inflation. In our model, inflation is driven by a combination of the Standard Model Higgs, the type II seesaw Higgs responsible for neutrino mass generation, and the unified symmetry-breaking Higgs field. During inflation, non-zero values of the latter field ensure the absence of the monopole problem. The baryon asymmetry is generated through the Affleck-Dine mechanism, facilitated by the non-zero angular motion in the phase of a complex scalar field, which is part of the inflaton. We find that the successful parameter region for generating baryon asymmetry through a renormalizable term in the scalar potential requires a rather heavy type II seesaw triplet, with a mass well beyond the TeV scale. Inflationary observables, in particular, the spectral index is in excellent agree with experimental observation, whereas tensor-to scalar ratio is expected to be probed by the future LiteBIRD and CMB-S4 missions., Comment: 9 pages, 3 figures and 1 table

The persistent anomalies observed in $B$ mesons related to $b \to s \mu^+ \mu^-$ transitions point to the potential for new physics beyond the Standard Model. To explore this further, we conduct an analysis of the exclusive semileptonic decays $B_c\to D_s^{(*)} \mu^+ \mu^-$ within an effective field theory formalism. We perform a global fit to new (axial)vector couplings using the current experimental data for $b \to s \mu^+ \mu^-$ transitions. We compute the branching ratios and physical observables such as forward-backward asymmetries, lepton polarization asymmetries, form factor independent observables for $B_c\to D_s^{(*)} \mu^+ \mu^-$, both within the Standard Model and in new physics scenarios. Additionally, we examine the existence of lepton flavor universality violation in the $B_c\to D_s^{(*)} \ell^+ \ell^-$ process., Comment: 25 Pages, 13 Figures

Researchers in biomedical research, public health, and the life sciences often spend weeks or months discovering, accessing, curating, and integrating data from disparate sources, significantly delaying the onset of actual analysis and innovation. Instead of countless developers creating redundant and inconsistent data pipelines, BioBricks.ai offers a centralized data repository and a suite of developer-friendly tools to simplify access to scientific data. Currently, BioBricks.ai delivers over ninety biological and chemical datasets. It provides a package manager-like system for installing and managing dependencies on data sources. Each 'brick' is a Data Version Control git repository that supports an updateable pipeline for extraction, transformation, and loading data into the BioBricks.ai backend at https://biobricks.ai. Use cases include accelerating data science workflows and facilitating the creation of novel data assets by integrating multiple datasets into unified, harmonized resources. In conclusion, BioBricks.ai offers an opportunity to accelerate access and use of public data through a single open platform., Comment: 23 pages, 2 figures

Discrepancies have been noted between experimental measurements and Standard Model predictions for various observables related to the $B\to(K, K^*,\phi) ll$ processes. Recently, the Belle-II Collaboration observed a $2.8\sigma$ deviation from Standard Model predictions in the branching ratio of the $B \to K \nu_l \bar{\nu_l}$ decay mode. In this context, we investigate the exclusive semileptonic $B_c\to D_s^{*}\mu^+ \mu^-$ decay mode, mediated by $b\to s$ quark level transition, using Effective Field Theory formalism. We perform a global fit to the new parameters using existing experimental data on $b \to s \mu^+ \mu^-$. We then estimate the branching ratio and other observables such as forward-backward asymmetry, lepton polarisation asymmetry, and the lepton non-universality parameter of the $B_c\to D_s^{(*)}\mu^+ \mu^-$ process. Although this process has not yet been detected experimentally, we offer predictions and discuss the observables for $B_c\to D_s^{*}\mu^+ \mu^-$ in both the Standard Model and potential new physics scenarios., Comment: 10 Pages, 2 Figures, Contribution to Proceedings of the 22nd Conference on Flavor Physics and CP Violation (FPCP 2024)

Transition metal dichalcogenides (TMDs) exhibit remarkable optical properties due to the diverse number of strongly bound excitons, which can be fine-tuned by alloying. Despite a flurry of research activity in characterizing these excitons, a comprehensive and profound understanding of their behavior with temperature is lacking. Here, we report the rich spectrum of excitonic features within bulk van der Waals alloy Mo$_{0.5}$W$_{0.5}$S$_2$ and Mo$_{0.5}$W$_{0.5}$Se$_2$ single crystals through temperature-dependent reflectance spectroscopy and first-principle calculations. We observed Rydberg excitons and interlayer excitons in both the single crystals. Notably, we provide the first experimental evidence of highly energetic A$^\prime$ and B$^\prime$ excitons in Mo$_{0.5}$W$_{0.5}$S$_2$ at room temperature. The strong carrier-phonon scattering significantly broadens the A$^\prime$, B$^\prime$ and interlayer excitons at room temperature in bulk Mo$_{0.5}$W$_{0.5}$S$_2$ single crystal compared to its selenide. Our findings, supported by density functional theory and Bethe-Salpeter equation calculations, signify the crucial role of carrier-phonon interactions. These results open pathways for next-generation optoelectronic devices and quantum technologies operating at high temperature.

We improve the backward compatibility of stableKanren to run miniKanren programs. stableKanren is a miniKanren extension capable of non-monotonic reasoning through stable model semantics. However, standard miniKanren programs that produce infinite results do not run as expected in stableKanren. According to stable model semantics, the contradictions are created by negations. A standard miniKanren's relations do not involve negation, and the coarse contradictions handling in stableKanren causes this compatibility issue. Therefore, we provide a find-grinded contradiction handling to restrict the checking scope. As a result, standard miniKanren relations can produce answers. We also add a ``run-partial'' interface so that standard miniKanren's relations implemented with ``define''/``defineo'' can generate answers even if they coexist with non-terminating or unsatisfiable stableKanren relations in the same environment. The ``run-partial'' interface also supports running stratified negation programs faster without checking global unavoidable contradictions. A dependency graph analysis can be applied to the input query in the future, so the ``run'' interface can implicitly decide whether to perform unavoidable contradictions checking to improve usability., Comment: 7 pages, 2 figures, ICFP '24 The miniKanren and Relational Programming Workshop

We integrate integrity constraints to stableKanren to enable a new problem-solving paradigm in combinatorial search problems. stableKanren extends miniKanren to reasoning about contradictions under stable model semantics. However, writing programs to solve combinatorial search problems in stableKanren did not fully utilize the contradiction reasoning. This is mainly due to the lack of control over the predicate (goal function) outcome during resolution. Integrity constraints defined by answer set programming (ASP) provide the ability to constrain the predicate outcome. However, integrity constraints are headless normal clauses, and stableKanren cannot create a goal function without a valid head. There are two approaches to handling integrity constraints, but they do not fit stableKanren. Therefore, we design a new approach to integrate integrity constraints into stableKanren. We show a uniform framework to solve combinatorial search problems using integrity constraints in extended stableKanren., Comment: 16 pages, 3 figures, ICFP '24 The miniKanren and Relational Programming Workshop

A one-size-fits-all paradigm that only adapts the scale and immediate outcome of climate investment to economic circumstances will provide a short-lived, economically inadequate response to climate issues; given the limited resources allocated to green finance, it stands to reason that the shortcomings of this will be exacerbated by the fact that it comes at the cost of long-term, self-perpetuating, systemic solutions. Financial commitments that do not consider the capital structure of green finance in an economy will cumulatively dis-aggregate the economic cost of climate investment, to erode the competitive advantage of the most innovative economies, while simultaneously imposing the greatest financial burden on economies that are most vulnerable to the impact of climate change; such disaggregation will also leave 'middle' economies in a state of flux - honouring similar financial commitments to vulnerable or highly developed peers, but unable to generate comparable return, yet sufficiently insulated from the impact of extreme climate phenomena to not organically develop solutions. In the face of these changing realities, green innovation needs to expand beyond technology and address systemic inefficiencies - lack of clear responsibility, ambiguously defined commitments, and inadequate checks & balances to name a few. Clever application of financial engineering demonstrates promise, and simple measures like carbon-credit exchanges have been effective in mitigating imperfections at the grassroots level. We believe that information- and incentive-centric systemic advancements can usher a fresh wave of green innovation that stands on the shoulders of giants to ensure effective implementation of technological breakthroughs; economic development that will create an international community equipped with a robust framework to deal with long-term crises in a strategic manner.

We conducted a comprehensive variability analysis of the blazar PKS 2255-282 using Fermi-LAT observations spanning over four years, from MJD 57783.5 to 59358.5. Our analysis revealed a transient quasi-periodic oscillation (QPO) with a period of 93$\pm$2.6 days. We employed a variety of Fourier-based methods, including the Lomb-Scargle Periodogram (LSP) and Weighted Wavelet Z-Transform (WWZ), as well as time domain analysis techniques such as Seasonal and Non-Seasonal Autoregressive Integrated Moving Average (ARIMA) models and the Stochastic modeling with Stochastically Driven Damped Harmonic Oscillator (SHO) models. Consistently, the QPO with a period of 93 days was detected across all methods used. The observed peak in LSP and time-averaged WWZ plots has a significance level of 4.06$\sigma$ and 3.96$\sigma$, respectively. To understand the source of flux modulations in the light curve, we explored various physical models. A plausible scenario involves the precession of the jet with a high Lorentz factor or the movement of a plasma blob along a helical trajectory within the relativistic jet., Comment: 13 pages, 13 Figures, accepted for publication in the Astrophysical Journal (APJ)

Climate change is a pressing global concern for governments, corporations, and citizens alike. This concern underscores the necessity for these entities to accurately assess the climate impact of manufacturing goods and providing services. Tools like process life cycle analysis (pLCA) are used to evaluate the climate impact of production, use, and disposal, from raw material mining through end-of-life. pLCA further enables practitioners to look deeply into material choices or manufacturing processes for individual parts, sub-assemblies, assemblies, and the final product. Reliable and detailed data on the life cycle stages and processes of the product or service under study are not always available or accessible, resulting in inaccurate assessment of climate impact. To overcome the data limitation and enhance the effectiveness of pLCA to generate an improved environmental impact profile, we are adopting an innovative strategy to identify alternative parts, products, and components that share similarities in terms of their form, function, and performance to serve as qualified substitutes. Focusing on enterprise electronics hardware, we propose a semi-supervised learning-based framework to identify substitute parts that leverages product bill of material (BOM) data and a small amount of component-level qualified substitute data (positive samples) to generate machine knowledge graph (MKG) and learn effective embeddings of the components that constitute electronic hardware. Our methodology is grounded in attributed graph embeddings and introduces a strategy to generate biased negative samples to significantly enhance the training process. We demonstrate improved performance and generalization over existing published models.

In this paper we propose a numerical procedure for the quantification of uncertainties in wave-structure interaction. We utilise the smoothed particle hydrodynamics (SPH) scheme for modelling the wave mechanics, coupled one-way with a finite element method (FEM) for the structural response. Physical wave flumes are extensively used in the study of hydrodynamics especially in wave-structure interaction (WSI) and prediction of forces to near-shore structures in disaster mitigation and offshore structures in the oil and gas, and more recently renewable energy sector. Over the years, numerical wave flumes have been developed extensively to enable the modelling of complex wave-structure interaction. However, most of these studies are deterministic and limited to using either simple flexible beams or rigid monolithic structures to model the structural part in the WSI. Additionally, uncertainties are commonly observed in both wave and structural parameters and need to be accounted for. This work presents a numerical framework to enable uncertainty quantification for wave-structure interaction problems in terms of the forces experienced by the structure. A one-way coupling between SPH with the FEM and uncertainty quantification (UQ) is proposed. We employ the so-called Tokyo wave flume geometry, which has potential for future surrogate modelling in WSI. The developed model is validated using numerical and experimental results from the literature and is used to demonstrate the prediction of probabilistic responses of structures under breaking and non-breaking wave scenarios.

In this paper we consider the modeling of measurement error for fund returns data. In particular, given access to a time-series of discretely observed log-returns and the associated maximum over the observation period, we develop a stochastic model which models the true log-returns and maximum via a L\'evy process and the data as a measurement error there-of. The main technical difficulty of trying to infer this model, for instance Bayesian parameter estimation, is that the joint transition density of the return and maximum is seldom known, nor can it be simulated exactly. Based upon the novel stick breaking representation of [12] we provide an approximation of the model. We develop a Markov chain Monte Carlo (MCMC) algorithm to sample from the Bayesian posterior of the approximated posterior and then extend this to a multilevel MCMC method which can reduce the computational cost to approximate posterior expectations, relative to ordinary MCMC. We implement our methodology on several applications including for real data.

Learning from Demonstrations, the field that proposes to learn robot behavior models from data, is gaining popularity with the emergence of deep generative models. Although the problem has been studied for years under names such as Imitation Learning, Behavioral Cloning, or Inverse Reinforcement Learning, classical methods have relied on models that don't capture complex data distributions well or don't scale well to large numbers of demonstrations. In recent years, the robot learning community has shown increasing interest in using deep generative models to capture the complexity of large datasets. In this survey, we aim to provide a unified and comprehensive review of the last year's progress in the use of deep generative models in robotics. We present the different types of models that the community has explored, such as energy-based models, diffusion models, action value maps, or generative adversarial networks. We also present the different types of applications in which deep generative models have been used, from grasp generation to trajectory generation or cost learning. One of the most important elements of generative models is the generalization out of distributions. In our survey, we review the different decisions the community has made to improve the generalization of the learned models. Finally, we highlight the research challenges and propose a number of future directions for learning deep generative models in robotics., Comment: 20 pages, 11 figures, submitted to TRO

SuperBIT was a 0.5-meter near-ultraviolet to near-infrared wide-field telescope that launched on a NASA superpressure balloon into the stratosphere from New Zealand for a 45-night flight. SuperBIT acquired multi-band images of galaxy clusters to study the properties of dark matter using weak gravitational lensing. We provide an overview of the instrument and its various subsystems. We then present the instrument performance from the flight, including the telescope and image stabilization system, the optical system, the power system, and the thermal system. SuperBIT successfully met the instrument's technical requirements, achieving a telescope pointing stability of 0.34 +/- 0.10 arcseconds, a focal plane image stability of 0.055 +/- 0.027 arcseconds, and a PSF FWHM of ~ 0.35 arcseconds over 5-minute exposures throughout the 45-night flight. The telescope achieved a near-diffraction limited point-spread function in all three science bands (u, b, and g). SuperBIT served as a pathfinder to the GigaBIT observatory, which will be a 1.34-meter near-ultraviolet to near-infrared balloon-borne telescope., Comment: 17 pages, 25 pages, published in the Astronomical Journal

We elucidate the recently observed discrepancy in the branching ratio of $B^{+}\rightarrow K^{+}$ +inv decay mode using a GeV scale scalar dark matter in an anomaly-free $U(1)_{B-L}$ gauge extension of the Standard Model. We constrain the new parameters by using consistency with the existing bounds on Dark matter relic density, direct detection, collider and BR($B\rightarrow K^*\nu\bar{\nu}$) measured at the Belle II experiment. We investigate couplings between the mediator and the SM fermions as well as the dark matter particle. We then estimate the branching ratios of $b \to s \nu \bar \nu$ decay processes such as $B\rightarrow (K^+,K^*)\nu\bar{\nu}$, $B_s\rightarrow (\eta,\eta')\nu\bar{\nu}$, $ B_s\rightarrow \phi\nu\bar{\nu}$ and $B_c\rightarrow (D_s, D_s^*)\nu\bar{\nu}$ in a common parameter space, meeting the current experimental bounds of both sectors simultaneously., Comment: 11 pages, 5 figures, Contribution to proceedings of the International Conference on Future Prospects in Neutrino and Astroparticle Physics (ICFPNAP 2024)

The integration of knowledge graphs and graph machine learning (GML) in genomic data analysis offers several opportunities for understanding complex genetic relationships, especially at the RNA level. We present a comprehensive approach for leveraging these technologies to analyze genomic variants, specifically in the context of RNA sequencing (RNA-seq) data from COVID-19 patient samples. The proposed method involves extracting variant-level genetic information, annotating the data with additional metadata using SnpEff, and converting the enriched Variant Call Format (VCF) files into Resource Description Framework (RDF) triples. The resulting knowledge graph is further enhanced with patient metadata and stored in a graph database, facilitating efficient querying and indexing. We utilize the Deep Graph Library (DGL) to perform graph machine learning tasks, including node classification with GraphSAGE and Graph Convolutional Networks (GCNs). Our approach demonstrates significant utility using our proposed tool, VariantKG, in three key scenarios: enriching graphs with new VCF data, creating subgraphs based on user-defined features, and conducting graph machine learning for node classification., Comment: arXiv admin note: substantial text overlap with arXiv:2312.04423

We present a method for developing navigation policies for multi-robot teams that interpret and follow natural language instructions. We condition these policies on embeddings from pretrained Large Language Models (LLMs), and train them via offline reinforcement learning with as little as 20 minutes of randomly-collected data. Experiments on a team of five real robots show that these policies generalize well to unseen commands, indicating an understanding of the LLM latent space. Our method requires no simulators or environment models, and produces low-latency control policies that can be deployed directly to real robots without finetuning. We provide videos of our experiments at https://sites.google.com/view/llm-marl.

In certain kinematic and particle mass configurations, triangle singularities may lead to line-shapes which mimic the effects of resonances. This well-known effect is scrutinized here in the presence of final-state rescattering. The goal is achieved first by utilizing general arguments provided by Landau equations, and second by applying a modern scattering formalism with explicit two- and three-body unitarity., Comment: 22 pages, 13 figures

In additive manufacturing (AM), particularly for laser-based metal AM, process optimization is crucial to the quality of products and the efficiency of production. The identification of optimal process parameters out of a vast parameter space, however, is a daunting task. Despite advances in simulations, the process optimization for specific materials and geometries is developed through a time-consuming trial-and-error approach, which often lacks the versatility to address multiple optimization objectives. Machine learning (ML) provides a powerful tool to accelerate the optimization process, but most current studies focus on simple single-track prints, which hardly translate to manufacturing 3D components for engineering applications. In this study, we develop an Accurate Inverse process optimization framework in laser Directed Energy Deposition (AIDED), based on machine learning models and a genetic algorithm, to aid process optimization in laser DED processes. Using the AIDED, we demonstrate the following: (i) Accurately predict single-track (R2 score 0.995), multi-track (R2 score 0.969), and multi-layer (1.07% and 10.75% error in width and height, respectively) cross-sectional melt pool geometries directly from process parameters; (ii) Determine appropriate hatch spacing and layer thickness for fabricating fully dense (density > 99.9%) multi-track and multi-layer prints; (iii) Inversely identify optimal process parameters directly from customizable application objectives within 1-3 hours. We also validate the effectiveness of the AIDED experimentally by achieving two exemplary targets: fast print speed and fine print resolution. Furthermore, we show the high transferability of the framework from stainless steel to pure nickel. With AIDED, we pave a new way for ''aiding'' the process optimization in the laser-based AM processes that is applicable to a wide range of materials.

The investigation of emerging non-toxic perovskite materials has been undertaken to advance the fabrication of environmentally sustainable lead-free perovskite solar cells. This study introduces a machine learning methodology aimed at predicting innovative halide perovskite materials that hold promise for use in photovoltaic applications. The seven newly predicted materials are as follows: CsMnCl$_4$, Rb$_3$Mn$_2$Cl$_9$, Rb$_4$MnCl$_6$, Rb$_3$MnCl$_5$, RbMn$_2$Cl$_7$, RbMn$_4$Cl$_9$, and CsIn$_2$Cl$_7$. The predicted compounds are first screened using a machine learning approach, and their validity is subsequently verified through density functional theory calculations. CsMnCl$_4$ is notable among them, displaying a bandgap of 1.37 eV, falling within the Shockley-Queisser limit, making it suitable for photovoltaic applications. Through the integration of machine learning and density functional theory, this study presents a methodology that is more effective and thorough for the discovery and design of materials.

In this paper we examine the numerical approximation of the limiting invariant measure associated with Feynman-Kac formulae. These are expressed in a discrete time formulation and are associated with a Markov chain and a potential function. The typical application considered here is the computation of eigenvalues associated with non-negative operators as found, for example, in physics or particle simulation of rare-events. We focus on a novel \emph{lagged} approximation of this invariant measure, based upon the introduction of a ratio of time-averaged Feynman-Kac marginals associated with a positive operator iterated $l \in\mathbb{N}$ times; a lagged Feynman-Kac formula. This estimator and its approximation using Diffusion Monte Carlo (DMC) have been extensively employed in the physics literature. In short, DMC is an iterative algorithm involving $N\in\mathbb{N}$ particles or walkers simulated in parallel, that undergo sampling and resampling operations. In this work, it is shown that for the DMC approximation of the lagged Feynman-Kac formula, one has an almost sure characterization of the $\mathbb{L}_1$-error as the time parameter (iteration) goes to infinity and this is at most of $\mathcal{O}(\exp\{-\kappa l\}/N)$, for $\kappa>0$. In addition a non-asymptotic in time, and time uniform $\mathbb{L}_1-$bound is proved which is $\mathcal{O}(l/\sqrt{N})$. We also prove a novel central limit theorem to give a characterization of the exact asymptotic in time variance. This analysis demonstrates that the strategy used in physics, namely, to run DMC with $N$ and $l$ small and, for long time enough, is mathematically justified. Our results also suggest how one should choose $N$ and $l$ in practice. We emphasize that these results are not restricted to physical applications; they have broad relevance to the general problem of particle simulation of the Feynman-Kac formula., Comment: 22 pages, 3 figures

This paper explores the adaptation of Transformerbased models for edge devices through the quantisation and hardware acceleration of the ARM Keyword Transformer (KWT) model on a RISC-V platform. The model was targeted to run on 64kB RAM in bare-metal C using a custom-developed edge AI library. KWT-1 was retrained to be 369 times smaller, with only a 10% loss in accuracy through reducing output classes from 35 to 2. The retraining and quantisation reduced model size from 2.42 MB to 1.65 kB. The integration of custom RISC-V instructions that accelerated GELU and SoftMax operations enabled a 5x speedup and thus ~5x power reduction in inference, with inference clock cycle counts decreasing from 26 million to 5.5 million clock cycles while incurring a small area overhead of approximately 29%. The results demonstrate a viable method for porting and accelerating Transformer-based models in low-power IoT devices., Comment: 6 pages, 7 figures, accepted to be published in the IEEE SOCC 2024 conference

Graph Neural Networks (GNNs) have attracted immense attention in the past decade due to their numerous real-world applications built around graph-structured data. On the other hand, Large Language Models (LLMs) with extensive pretrained knowledge and powerful semantic comprehension abilities have recently shown a remarkable ability to benefit applications using vision and text data. In this paper, we investigate how LLMs can be leveraged in a computationally efficient fashion to benefit rich graph-structured data, a modality relatively unexplored in LLM literature. Prior works in this area exploit LLMs to augment every node features in an ad-hoc fashion (not scalable for large graphs), use natural language to describe the complex structural information of graphs, or perform computationally expensive finetuning of LLMs in conjunction with GNNs. We propose E-LLaGNN (Efficient LLMs augmented GNNs), a framework with an on-demand LLM service that enriches message passing procedure of graph learning by enhancing a limited fraction of nodes from the graph. More specifically, E-LLaGNN relies on sampling high-quality neighborhoods using LLMs, followed by on-demand neighborhood feature enhancement using diverse prompts from our prompt catalog, and finally information aggregation using message passing from conventional GNN architectures. We explore several heuristics-based active node selection strategies to limit the computational and memory footprint of LLMs when handling millions of nodes. Through extensive experiments & ablation on popular graph benchmarks of varying scales (Cora, PubMed, ArXiv, & Products), we illustrate the effectiveness of our E-LLaGNN framework and reveal many interesting capabilities such as improved gradient flow in deep GNNs, LLM-free inference ability etc.

We present Crab X-ray polarization measurements using IXPE data with a total exposure of 300ks, three times more than the initial 2022 discovery paper. Polarization is detected in three times more pulsar phase bins, revealing an S-shaped $+40^\circ$ polarization angle sweep in the main pulse and ${>}1\sigma$ departures from the OPTIMA optical polarization in both pulses, suggesting different radiation mechanisms or sites for the polarized emission at the two wavebands. Our polarization map of the inner nebula reveals a toroidal magnetic field, as seen in prior IXPE analyses. Along the southern jet, the magnetic field orientation relative to the jet axis changes from perpendicular to parallel and the polarization degree decreases by ${\sim}6\%$. These observations may be explained by kink instabilities along the jet or a collision with a dense, jet-deflecting medium at the tip. Using spectropolarimetric analysis, we find asymmetric polarization in the four quadrants of the inner nebula, as expected for a toroidal field geometry, and a spatial correlation between polarization degree and photon index., Comment: 14 pages, 7 figures, 3 tables. Accepted by The Astrophysical Journal

InAs semiconductor quantum dots (QDs) emitting in the near infrared are promising platforms for on-demand single-photon sources and spin-photon interfaces. However, the realization of quantum-photonic nanodevices emitting in the second and third telecom windows with similar performance remains an open challenge. Here, we report an optimized heterostructure design for QDs emitting in the O- and C-bands grown by means of molecular beam epitaxy. The InAs QDs are grown on compositionally graded InGaAs buffers, which act as virtual substrates, and are embedded in mostly relaxed active regions. Reciprocal space maps of the indium profiles and optical absorption spectra are used to optimize In0.22Ga0.78As and In0.30Ga0.70As active regions, accounting for the chosen indium grading profile. This approach results in a tunable QD photoluminescence (PL) emission from 1200 up to 1600 nm. Power and polarization dependent micro-PL measurements performed at 4 K reveal exciton-biexciton complexes from quantum dots emitting in the telecom O- and C-bands. The presented study establishes a flexible platform that can be an essential component for advanced photonic devices based on InAs/GaAs that serve as building blocks for future quantum networks.

In this work, we propose a management method for controlling the speed and direction of self-bound quantum droplets (QDs) in a binary Bose-Einstein condensate mixture under time-modulated external harmonic confinement. Utilizing the 1D extended Gross-Pit"{a}evskii equation, QDs are constructed within both regular and expulsive parabolic traps, considering temporally varying attractive quadratic beyond mean field and repulsive cubic mean-field atom-atom interactions. Through the derived wavefunction solution, we illustrate the dynamics of slowing, stopping, reversing, fragmentation, collapse, and revival of droplets. Additionally, the solutions reveal a crystalline order with a superfluid background, indicative of supersolid behavior in various parameter domains. Notably, one-third of the constant background matches the lowest residual condensate. These findings hold potential applications in matter-wave interferometry and quantum information processing.

Modern Large Language Models (LLMs) are composed of matrices with billions of elements, making their storage and processing quite demanding in terms of computational resources and memory usage. Being significantly large, such matrices can often be expressed in low-rank format with potential to relax resource requirements. Unlike prior works which focus on developing novel matrix decomposition algorithms, in this work we first study the emergence of low-rank structures across matrices within different layers of LLMs and establish a consequential relationship between the gradient dynamics and emerging low-rank expressiveness of matrices. Our findings reveal that different layers exhibit varying levels of converged low-rank structure, necessitating a non-uniform rank reduction across them to minimize performance drop due to compression. In view of that, we present Weight Low-Rank Projection (WeLore) that unifies weight compression and memory-efficient fine-tuning as ONE, in a data-agnostic and one-shot way. WeLore capitalizes the heavy-tail distribution of singular values to identify a suitable rank reduction ratio for matrices within LLMs. Going beyond only as a compression technique, WeLore categorizes weight matrices into Low-rank Components (LRCs) and Non-Low-rank Components (N-LRCs) based on their ability to express themselves as low-rank. Our gradient perspective and extensive experiments illustrate that LRCs tend to have better finetuning capabilities and can closely mimic (sometimes outperform) the training loss trajectory and performance of full-finetuning with notable memory and compute footprint reduction. For example, finetuning a 50\% compressed LLaMa-2 7B model using only a fraction of parameters in LRCs (WeLore) can outperform its full finetuning with ~3x better throughput and ~0.6x GPU requirement. Our codes are available at \url{https://github.com/VITA-Group/welore}

We present multiwavelength polarization measurements of the luminous blazar Mrk~501 over a 14-month period. The 2--8 keV X-ray polarization was measured with the Imaging X-ray Polarimetry Explorer (IXPE) with six 100-ks observations spanning from 2022 March to 2023 April. Each IXPE observation was accompanied by simultaneous X-ray data from NuSTAR, Swift/XRT, and/or XMM-Newton. Complementary optical-infrared polarization measurements were also available in the B, V, R, I, and J bands, as were radio polarization measurements from 4.85 GHz to 225.5 GHz. Among the first five IXPE observations, we did not find significant variability in the X-ray polarization degree and angle with IXPE. However, the most recent sixth observation found an elevated polarization degree at $>3\sigma$ above the average of the other five observations. The optical and radio measurements show no apparent correlations with the X-ray polarization properties. Throughout the six IXPE observations, the X-ray polarization degree remained higher than, or similar to, the R-band optical polarization degree, which remained higher than the radio value. This is consistent with the energy-stratified shock scenario proposed to explain the first two IXPE observations, in which the polarized X-ray, optical, and radio emission arises from different regions., Comment: Accepted for publication in ApJ

The Super-pressure Balloon-borne Imaging Telescope (SuperBIT) is a near-diffraction-limited 0.5m telescope that launched via NASA's super-pressure balloon technology on April 16, 2023. SuperBIT achieved precise pointing control through the use of three nested frames in conjunction with an optical Fine Guidance System (FGS), resulting in an average image stability of 0.055" over 300-second exposures. The SuperBIT FGS includes a tip-tilt fast-steering mirror that corrects for jitter on a pair of focal plane star cameras. In this paper, we leverage the empirical data from SuperBIT's successful 45-night stratospheric mission to inform the FGS design for the next-generation balloon-borne telescope. The Gigapixel Balloon-borne Imaging Telescope (GigaBIT) is designed to be a 1.35m wide-field, high resolution imaging telescope, with specifications to extend the scale and capabilities beyond those of its predecessor SuperBIT. A description and analysis of the SuperBIT FGS will be presented along with methodologies for extrapolating this data to enhance GigaBIT's FGS design and fine pointing control algorithm. We employ a systems engineering approach to outline and formalize the design constraints and specifications for GigaBIT's FGS. GigaBIT, building on the SuperBIT legacy, is set to enhance high-resolution astronomical imaging, marking a significant advancement in the field of balloon-borne telescopes., Comment: 13 pages, 7 figures, SPIE Astronomical Telescopes + Instrumentation 2024

To follow up on the unexpectedly-good performance of several coupled-cluster models with approximate inclusion of 3-body clusters [J. Chem. Phys. 151, 064102 (2019)] we performed a more complete assessment of the 3CC method [J. Chem. Phys. 125, 204105 (2006)] for accurate computational thermochemistry in the standard HEAT framework. New spin-integrated implementation of the 3CC method applicable to closed- and open-shell systems utilizes a new automated toolchain for derivation, optimization, and evaluation of operator algebra in many-body electronic structure. We found that with a double-zeta basis set the 3CC correlation energies and their atomization energy contributions are almost always more accurate (with respect to the CCSDTQ reference) than the CCSDT model as well as the standard CCSD(T) model. The mean absolute errors in cc-pVDZ {3CC, CCSDT, and CCSD(T)} electronic (per valence electron) and atomization energies relative to the CCSDTQ reference for the HEAT dataset [J. Chem. Phys. 121, 11599 (2004)], were {24, 70, 122} $\mu E_h/e$ and {0.46, 2.00, 2.58} kJ/mol, respectively. The mean absolute errors in the complete-basis-set limit {3CC, CCSDT, and CCSD(T)} atomization energies relative to the HEAT model reference, were {0.52, 2.00, and 1.07} kJ/mol, The significant and systematic reduction of the error by the 3CC method and its lower cost than CCSDT suggests it as a viable candidate for post-CCSD(T) thermochemistry applications, as well as the preferred alternative to CCSDT in general., Comment: 30 pages, 3 tables

Training Large Language Models (LLMs) is memory-intensive due to the large number of parameters and associated optimization states. GaLore, a recent method, reduces memory usage by projecting weight gradients into a low-rank subspace without compromising performance. However, GaLore relies on time-consuming Singular Value Decomposition (SVD) operations to identify the subspace, and the frequent subspace updates lead to significant training time overhead. Moreover, GaLore offers minimal improvements in accuracy and efficiency compared to LoRA in more accessible fine-tuning scenarios. To address these limitations, we introduce Q-Galore, a novel approach that substantially reduces memory usage by combining quantization and low-rank projection, surpassing the benefits of GaLore. Our method is based on two key observations: (i) the gradient subspace exhibits diverse properties, with some layers converging early in training while others are subject to frequent changes; (ii) the projection matrices are highly resilient to low-bit quantization. Leveraging these insights, Q-GaLore adaptively updates the gradient subspace based on its convergence statistics, achieving comparable performance while significantly reducing the number of SVD operations. We maintain the projection matrices in INT4 format and weights in INT8 format, incorporating stochastic rounding to capture accumulated gradient information. This approach enables a high-precision training trajectory using only low-precision weights. We demonstrate that Q-GaLore achieves highly competitive performance with exceptional memory efficiency. At pre-training, Q-GaLore facilitates training a LLaMA-7B model from scratch on a single NVIDIA RTX 4060 Ti with only 16 GB memory. At fine-tuning, it reduces memory consumption by up to 50% compared to LoRA and GaLore, while consistently outperforming QLoRA at the same memory cost.

Neutron stars and white dwarfs are both dense remnants of post-main-sequence stars. Pulsars, magnetars and strongly magnetised white dwarfs have all been seen to been observed to exhibit coherent, pulsed radio emission in relation to their rotational period. Recently, a new type of radio long period transient (LPT) has been discovered. The bright radio emission of LPTs resembles that of radio pulsars and magnetars. However, they pulse on timescales (minutes) much longer than previously seen. While minute timescales are common rotation periods for white dwarfs, LPTs are much brighter than the known pulsating white dwarfs, and dipolar radiation from isolated (as opposed to binary) magnetic white dwarfs has yet to be observed. Here, we report the discovery of a new $\sim$421~s LPT, CHIME J0630+25, using the CHIME/FRB and CHIME/Pulsar instruments. We used standard pulsar timing techniques and obtained a phase-coherent timing solution which yielded limits on the inferred magnetic field and characteristic age. CHIME J0630+25 is remarkably nearby ($170 \pm 80$~pc), making it the closest LPT discovered to date., Comment: Submitted

We present the most comprehensive catalog to date of Type I Superluminous Supernovae (SLSNe), a class of stripped envelope supernovae (SNe) characterized by exceptionally high luminosities. We have compiled a sample of 262 SLSNe reported through 2022 December 31. We verified the spectroscopic classification of each SLSN and collated an exhaustive data set of UV, optical and IR photometry from both publicly available data and our own FLEET observational follow-up program, totaling over 30,000 photometric detections. Using these data we derive observational parameters such as the peak absolute magnitudes, rise and decline timescales, as well as bolometric luminosities, temperature and photospheric radius evolution for all SLSNe. Additionally, we model all light curves using a hybrid model that includes contributions from both a magnetar central engine and the radioactive decay of $^{56}$Ni. We explore correlations among various physical and observational parameters, and recover the previously found relation between ejecta mass and magnetar spin, as well as the overall progenitor pre-explosion mass distribution with a peak at $\approx 6.5$ M$_\odot$. We find no significant redshift dependence for any parameter, and no evidence for distinct sub-types of SLSNe. We find that $< 3$\% of SLSNe are best fit with a significant contribution from radioactive decay $\gtrsim 50$\%, representing a set of relatively dim and slowly declining SNe. We provide several analytical tools designed to simulate typical SLSN light curves across a broad range of wavelengths and phases, enabling accurate K-corrections, bolometric scaling calculations, and inclusion of SLSNe in survey simulations or future comparison works. The complete catalog, including all of the photometry, models, and derived parameters, is made available as an open-source resource on GitHub., Comment: 59 pages, 22 Figures, Submitted to MNRAS

X-ray polarimetry of solar flares is still a not well established field of observation of our star. Past polarimeters were not able to measure with a high significance the polarization in X-rays from solar flares. Moreover, they had no imaging capabilities and measured only the polarization by integrating on all the image of the source. We propose a mission concept based on a gas photoelectric polarimeter, coupled with multilayer lobster-eye optics, to perform imaging-spectro-polarimetry of solar flares while monitoring the entire solar disc., Comment: Proceeding of SPIE Conference "Astronomical Telescopes+ Instrumentation", Yokohama (Japan), 16-21 June 2024

We present several enhancements to the open-source ESP platform to support flexible and efficient on-chip communication for programmable accelerators in heterogeneous SoCs. These enhancements include 1) a flexible point-to-point communication mechanism between accelerators, 2) a multicast NoC that supports data forwarding to multiple accelerators simultaneously, 3) accelerator synchronization leveraging the SoC's coherence protocol, 4) an accelerator interface that offers fine-grained control over the communication mode used, and 5) an example ISA extension to support our enhancements. Our solution adds negligible area to the SoC architecture and requires minimal changes to the accelerators themselves. We have validated most of these features in complex FPGA prototypes and plan to include them in the open-source release of ESP in the coming months., Comment: Appeared in the Sixth International Workshop on Domain Specific System Architecture (DOSSA-6)

Large Visual Language Models (LVLMs) struggle with hallucinations in visual instruction following task(s), limiting their trustworthiness and real-world applicability. We propose Pelican -- a novel framework designed to detect and mitigate hallucinations through claim verification. Pelican first decomposes the visual claim into a chain of sub-claims based on first-order predicates. These sub-claims consist of (predicate, question) pairs and can be conceptualized as nodes of a computational graph. We then use Program-of-Thought prompting to generate Python code for answering these questions through flexible composition of external tools. Pelican improves over prior work by introducing (1) intermediate variables for precise grounding of object instances, and (2) shared computation for answering the sub-question to enable adaptive corrections and inconsistency identification. We finally use reasoning abilities of LLM to verify the correctness of the the claim by considering the consistency and confidence of the (question, answer) pairs from each sub-claim. Our experiments reveal a drop in hallucination rate by $\sim$8%-32% across various baseline LVLMs and a 27% drop compared to approaches proposed for hallucination mitigation on MMHal-Bench. Results on two other benchmarks further corroborate our results.

Machine ethics is the field that studies how ethical behaviour can be accomplished by autonomous systems. While there exist some systematic reviews aiming to consolidate the state of the art in machine ethics prior to 2020, these tend to not include work that uses reinforcement learning agents as entities whose ethical behaviour is to be achieved. The reason for this is that only in the last years we have witnessed an increase in machine ethics studies within reinforcement learning. We present here a systematic review of reinforcement learning for machine ethics and machine ethics within reinforcement learning. Additionally, we highlight trends in terms of ethics specifications, components and frameworks of reinforcement learning, and environments used to result in ethical behaviour. Our systematic review aims to consolidate the work in machine ethics and reinforcement learning thus completing the gap in the state of the art machine ethics landscape

One of the fundamental interactions that is found in many functional materials is the spin-phonon coupling (SPC), which is at the heart of many novel functionalities. The simultaneous presence of multi-magnetic phases makes SPC even more intriguing. We have used Raman spectroscopy as well as first-principles methods to investigate the possibility of the appearance of SPC in Gd-doped Y$_2$CoMnO$_6$ (YGCMO) double perovskite oxide and the influence of anti-site disorder on the same. YGCMO is found to exhibit anti-site disorder leading to both ferromagnetic (between Co and Mn) and anti-ferromagnetic interactions (Co-Co, Mn-Mn, Gd-Co/Mn). An analysis of the temperature-dependent phonon frequency for the stretching modes of YGCMO, obtained using RAMAN spectroscopy, indicates that SPC is possibly emerging from simultaneous presence of ferromagnetic and antiferromagnetic interactions. The nature of the phonon linewidth and the insulating state of the material eliminate the role of magnetostriction on the observed anomaly. The spin-phonon coupling strength comes out to be 0.29 cm$^{-1}$. Our experimental findings are corroborated by first-principles DFT calculations which indicate the presence of SPC in ordered YGCMO getting enhanced in the presence of anti-site disorder. This indicates a strong influence of B-site (Co/Mn) ordering on SPC in the bulk double perovskite systems. An analysis of the cause behind the enhanced SPC in the presence of anti-site disorder is also presented., Comment: 23 pages, 14 figures

Following recent experimental progress in the study of bottom baryons, we systematically calculate the mass spectra of $\Lambda_{b}$, $\Xi_{b}$, $\Sigma_{b}$, $\Xi_{b}^{'}$, and $\Omega_{b}$ baryons with a quark-diquark picture in the framework of a relativistic flux tube model with spin-dependent interactions in the j-j coupling scheme. Furthermore, we calculate the strong decay width of bottom baryons decaying into a bottom baryon and a light pseudoscalar meson. A good agreement is found between the calculated masses and the experimentally available masses of singly bottom baryons. %We interpret $\Sigma_{b}(6097)$ as a $1P(3/2^{-})$ state, $\Xi_{b}(6100)$ as $1P(1/2^{-})$ state of $\Xi_{b}$ baryon, $\Xi_{b}(6227)$ as a $1P(1/2^{-})$ or $1P(3/2^{-})$ state of $\Xi_{b}'$ baryon, $\Xi_{b}(6327)$ as a $1P(3/2^{-})$ state of $\Xi_{b}'$ baryon, and $\Xi_{b}(6333)$ as a $1P(3/2^{-})$ state of $\Xi_{b}'$ baryon. By analysing both mass spectra and strong decay widths, we interpret $\Sigma_{b}(6097)$ as a $1P(3/2^{-})$ state and $\Xi_{b}(6100)$ as a $1P(1/2^{-})$ state of $\Xi_{b}$ baryon. The $\Xi_{b}(6227)$ is identified to be an orbital excitation $1P$ of the $\Xi_{b}^{'}$ baryon with $J^{P}=3/2^{-}$. Further, we determine $\Xi_{b}(6327)$ and $\Xi_{b}(6333)$ as a $1P(3/2^{-})$ state and $1P(5/2^{-})$ state, respectively, of $\Xi_{b}^{'}$ baryon. From the obtained mass spectra, we construct the Regge trajectories in the $(J,M^{2})$ plane, which are found to be essentially linear, parallel, and equidistant. Our predictions for higher orbital and radial excited states can help experimentalists identify missing excited states of singly bottom baryons., Comment: 21 pages, 7 figures, Submitted in PRD

In task-oriented conversational AI evaluation, unsupervised methods poorly correlate with human judgments, and supervised approaches lack generalization. Recent advances in large language models (LLMs) show robust zeroshot and few-shot capabilities across NLP tasks. This paper explores using LLMs for automated dialogue quality evaluation, experimenting with various configurations on public and proprietary datasets. Manipulating factors such as model size, in-context examples, and selection techniques, we examine "chain-of-thought" (CoT) reasoning and label extraction procedures. Our results show that (1) larger models yield more accurate dialogue labels; (2) algorithmic selection of in-context examples outperforms random selection; (3) CoT reasoning where an LLM is asked to provide justifications before outputting final labels improves performance; and (4) fine-tuned LLMs outperform out-of-the-box ones. Our results indicate that LLMs that are suitably fine-tuned and have sufficient reasoning capabilities can be leveraged for automated dialogue evaluation.

We developed DyGETViz, a novel framework for effectively visualizing dynamic graphs (DGs) that are ubiquitous across diverse real-world systems. This framework leverages recent advancements in discrete-time dynamic graph (DTDG) models to adeptly handle the temporal dynamics inherent in dynamic graphs. DyGETViz effectively captures both micro- and macro-level structural shifts within these graphs, offering a robust method for representing complex and massive dynamic graphs. The application of DyGETViz extends to a diverse array of domains, including ethology, epidemiology, finance, genetics, linguistics, communication studies, social studies, and international relations. Through its implementation, DyGETViz has revealed or confirmed various critical insights. These include the diversity of content sharing patterns and the degree of specialization within online communities, the chronological evolution of lexicons across decades, and the distinct trajectories exhibited by aging-related and non-related genes. Importantly, DyGETViz enhances the accessibility of scientific findings to non-domain experts by simplifying the complexities of dynamic graphs. Our framework is released as an open-source Python package for use across diverse disciplines. Our work not only addresses the ongoing challenges in visualizing and analyzing DTDG models but also establishes a foundational framework for future investigations into dynamic graph representation and analysis across various disciplines., Comment: 27 pages, 11 figures

The goal of text style transfer is to transform the style of texts while preserving their original meaning, often with only a few examples of the target style. Existing style transfer methods generally rely on the few-shot capabilities of large language models or on complex controllable text generation approaches that are inefficient and underperform on fluency metrics. We introduce TinyStyler, a lightweight but effective approach, which leverages a small language model (800M params) and pre-trained authorship embeddings to perform efficient, few-shot text style transfer. We evaluate on the challenging task of authorship style transfer and find TinyStyler outperforms strong approaches such as GPT-4. We also evaluate TinyStyler's ability to perform text attribute style transfer (formal $\leftrightarrow$ informal) with automatic and human evaluations and find that the approach outperforms recent controllable text generation methods. Our model has been made publicly available at https://huggingface.co/tinystyler/tinystyler .

Repeating fast radio bursts can exhibit a wide range of burst repetition rates, from none to hundreds of bursts per hour. Here, we report the detection and characteristics of 57 bursts from the recently discovered FRB 20240114A, observed with GMRT in the frequency ranges 300-500 MHz and 550-750 MHz. Majority of the bursts show narrow emission-bandwidth with $\Delta\nu/\nu \sim$ around 10 %. All of the bursts we detect are faint ($<$10 Jy ms), and thus probe the lower end of the energy distribution. We determine the rate function for FRB 20240114A at 400 MHz, and downward drift rates at 400 and 650 MHz, and discuss our measurements in the context of the repeating FRB population. We observe sudden variations in the burst activity of FRB 20240114A over time. Our data as well as the other publicly available information on other observations of FRB 20240114A so far, there is an indication that FRB 20240114A potentially exhibit a chromaticity in its burst activity. While the burst properties of FRB 20240114A are similar to ther repeating FRBs, the frequency-dependent activity, if established, could provide crucial clues to the origin of repeating FRBs. We also place the most stringent 5$\sigma$ upper limits of 600 $\mu$Jy and 89 $\mu$Jy on any persistent radio source (PRS) associated with FRB 20240114A at 400 MHz and 650 MHz, respectively, and compare these with the luminosity of the known PRSs associated with FRB121102A and FRB190520B., Comment: 13 Pages, 5 Figures, Submitted to ApJ

We present the first X-ray spectropolarimetric results for Cygnus X-1 in its soft state from a campaign of five IXPE observations conducted during 2023 May-June. Companion multiwavelength data during the campaign are likewise shown. The 2-8 keV X-rays exhibit a net polarization degree PD=1.99%+/-0.13% (68% confidence). The polarization signal is found to increase with energy across IXPE's 2-8 keV bandpass. The polarized X-rays exhibit an energy-independent polarization angle of PA=-25.7+/-1.8 deg. East of North (68% confidence). This is consistent with being aligned to Cyg X-1's AU-scale compact radio jet and its pc-scale radio lobes. In comparison to earlier hard-state observations, the soft state exhibits a factor of 2 lower polarization degree, but a similar trend with energy and a similar (also energy-independent) position angle. When scaling by the natural unit of the disk temperature, we find the appearance of a consistent trendline in the polarization degree between soft and hard states. Our favored polarimetric model indicates Cyg X-1's spin is likely high (a* above ~0.96). The substantial X-ray polarization in Cyg X-1's soft state is most readily explained as resulting from a large portion of X-rays emitted from the disk returning and reflecting off the disk surface, generating a high polarization degree and a polarization direction parallel to the black hole spin axis and radio jet. In IXPE's bandpass, the polarization signal is dominated by the returning reflection emission. This constitutes polarimetric evidence for strong gravitational lensing of X-rays close to the black hole., Comment: 20 pages, accepted for publication in ApJL

Recent observations of X-ray pulsars (XRPs) performed by the Imaging X-ray Polarimetry Explorer (IXPE) have made it possible to investigate the intricate details of these objects in a new way, thanks to the added value of X-ray polarimetry. Here we present the results of the IXPE observations of SMC X-1, a member of the small group of XRPs displaying super-orbital variability. SMC X-1 was observed by IXPE three separate times during the high state of its super-orbital period. The observed luminosity in the 2-8 keV energy band of $L=2\times10^{38}$ erg/s makes SMC X-1 the brightest XRP ever observed by IXPE. We detect significant polarization in all three observations, with values of the phase-averaged polarization degree (PD) and polarization angle (PA) of $3.2\pm0.8$% and $97\deg\pm8\deg$ for Observation 1, $3.0\pm0.9$% and $90\deg\pm8\deg$ for Observation 2, and $5.5\pm1.1$% and $80\deg\pm6\deg$ for Observation 3, for the spectro-polarimetric analysis. The observed PD shows an increase over time with decreasing luminosity, while the PA decreases in decrements of 10\deg. The phase-resolved spectro-polarimetric analysis reveals significant detection of polarization in three out of seven phase bins, with the PD ranging between 2% and 10%, and a corresponding range in the PA from $\sim$70\deg\ to $\sim$100\deg. The pulse-phase resolved PD displays an apparent anti-correlation with the flux. Using the rotating vector model, we obtain constraints on the pulsar's geometrical properties for the individual observations. The position angle of the pulsar displays an evolution over time supporting the idea that we observe changes related to different super-orbital phases. Scattering in the wind of the precessing accretion disk may be responsible for the behavior of the polarimetric properties observed during the high-state of SMC X-1's super-orbital period., Comment: 11 pages, 11 figures, submitted to A&A

We describe a new exact lattice bosonization of matrix quantum mechanics (equivalently of non-relativistic fermions) that is valid for arbitrary rank N of the matrix, based on an exact operator bosonization introduced earlier in [1]. The trace identities are automatically incorporated in this formalism. The finite number N of fermions is reflected in the finite number N of bosonic oscillators, or equivalently to the finite number N of lattice points. The fermion Hamiltonian is exactly mappable to a bosonic Hamiltonian. At large N, the latter becomes local and corresponds to the lattice version of a relativistic boson Hamiltonian, with a lattice spacing of order 1/N. The finite lattice spacing leads to a finite entanglement entropy (EE) of the bosonic theory, which reproduces the finite EE of the fermionic theory. Such a description is not available in the standard bosonization in terms of fermion density fluctuations on the Fermi surface, which does not have a built-in short distance cut-off (see, however, [2]). The bosonic lattice is equipped with a geometry determined by the matrix potential or equivalently by the shape of the Fermi surface. Our bosonization also works in the double scaled c=1 model, where the bosonic EE again turns out to be finite, with the short distance cut-off turning as g_s l_s, and reproduces the matrix result. Once again, such a short distance cut-off cannot appear in the conventional dual of c=1 in terms of the 2D string ``tachyon'', where the expected short distance scale is l_s. This indicates our bosonization as a possibly different dual description to the c=1 matrix model appropriate for ``local physics'' like quantum entanglement, by contrast with the conventional duality to the eigenvalue density which works well for asymptotic observables like S-matrices. We briefly discuss possible relation of our bosonization to D0 branes., Comment: 25 pages + appendices, 9 figures (v1)

Supercooled first-order phase transition (FOPT) can lead to the formation of primordial black holes (PBHs). This scenario imposes stringent requirements on the profile of the effective potential. In this work, we use the singlet extended Standard Model (xSM) as a benchmark model to investigate this possibility at the electroweak scale. The PBHs formed during a supercooled FOPT have a narrow mass distribution around the mass of Earth. This distribution is closely tied to the temperature at which the PBHs form, corresponding to the FOPT at the electroweak scale. This scenario can be probed with microlensing experiments, space-based gravitational wave detectors, and collider experiments. Remarkably, the future space-based gravitational wave detector LISA will hold the potential to either confirm this PBH scenario in the xSM or completely rule it out for extremely small total dark matter fraction made of PBHs, down to $f_{\rm PBH}> 10^{-300}$. Interestingly, our findings suggest that PBHs within the xSM framework may align with observations of the six ultrashort timescale events reported by the OGLE microlensing experiment., Comment: 46 pages, 12 figures and 1 table