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We report on the characteristics of a microwave photon counter device based on a superconducting transmon qubit. Its design is similar to [arXiv:2307.03614], with an additional bandwidth tuning circuit that allows optimizing the device efficiency and noise. Owing to this new feature and to improvements in device fabrication, a power sensitivity of $3 \cdot 10^{-23} \mathrm{W}/\sqrt{\mathrm{Hz}}$ is reached. We confirm the high performance of the device by measuring single spin microwave fluorescence., Comment: 12 pages, 10 figures

Antiferromagnetic (AF) skyrmions are topological spin structures with fully compensated, net-zero magnetization. Compared to their ferromagnetic (FM) skyrmion counterparts, their reduced stray field and enhanced electrical response can enable linear, high-throughput current-driven motion. However, their bubble-like character in conventional bilayer AFs limits their stability to fluctuations, leading to deformation and annihilation. Here we present the engineering of a composite AF chiral multilayer, wherein the interplay of AF and FM interlayer couplings generates compensated skyrmions with compact structures. High-resolution magnetic imaging and micromagnetic simulations show that the internal exchange field stabilizes AF skyrmions at zero external field with characteristics comparable to FM counterparts at 100 mT. Quantitative analyses establish their decoupling above the exchange field, yielding independent, spatially segregated textures in constituent chiral layers. This work provides a foundation to develop AF spin-textures with enhanced immunity, compatible with efficient readout and manipulation, with relevance to unconventional computing., Comment: 9 pages, 5 figures

Advancement of solid state electrolytes (SSEs) for all solid state batteries typically focuses on modification of a parent structural framework for improved conductivity, \textit{e.g.} cation substitution for an immobile ion or varying the concentration of the mobile ion. Therefore, novel frameworks can be disruptive by enabling fast ion conduction aided by different structure and diffusion mechanisms, and unlocking optimal conductors with different properties (\textit{e.g.} mechanical properties, sintering needs, electrochemical stability) than previously published. Herein, we perform a high throughput survey of an understudied structural framework for sodium ion conduction, Na$_{8-x}$A$^x$P$_2$O$_9$ (NAP), to understand the family's thermodynamic stability, synthesizability, and ionic conduction. We first show that the parent phase Na$_4$TiP$_2$O$_9$ (NTP) undergoes a structural distortion (with accompanying conductivity transition) due to unstable phonons from a pseduo-Jahn Teller mode in the 1D titanium chains. Then, screening of cation-substituted structural candidates with \textit{ab initio} and machine-learned potential calculations reveal a number of candidates that are thermodynamically stable, likely synthesizable, and have high predicted ionic conductivities. High throughput experimental trials and subsequent methodology optimization of one Na$_4$SnP$_2$O$_9$ (NSP) highlight collective challenges to the synthesis pathways for sodium phosphate materials via solid state synthesis. Our results demonstrate that NAP is a highly tunable conduction framework whose high temperature conductivity transition has heretofore eliminated it from significant research interest. By expanding the structural toolkit for SSE design, we increase the number of useful sodium ion electrolytes for integration into safe and accessible solid state batteries., Comment: 15 page manuscript, 42 total pages

A broad range of technologies rely on remote inference, wherein data acquired is conveyed over a communication channel for inference in a remote server. Communication between the participating entities is often carried out over rate-limited channels, necessitating data compression for reducing latency. While deep learning facilitates joint design of the compression mapping along with encoding and inference rules, existing learned compression mechanisms are static, and struggle in adapting their resolution to changes in channel conditions and to dynamic links. To address this, we propose Adaptive Rate Task-Oriented Vector Quantization (ARTOVeQ), a learned compression mechanism that is tailored for remote inference over dynamic links. ARTOVeQ is based on designing nested codebooks along with a learning algorithm employing progressive learning. We show that ARTOVeQ extends to support low-latency inference that is gradually refined via successive refinement principles, and that it enables the simultaneous usage of multiple resolutions when conveying high-dimensional data. Numerical results demonstrate that the proposed scheme yields remote deep inference that operates with multiple rates, supports a broad range of bit budgets, and facilitates rapid inference that gradually improves with more bits exchanged, while approaching the performance of single-rate deep quantization methods., Comment: 13 pages, 12 figures

Common features of sub-Neptunes atmospheres observed to date include signatures of aerosols at moderate equilibrium temperatures (~500-800 K), and a prevalence of high mean molecular weight atmospheres, perhaps indicating novel classes of planets such as water worlds. Here we present a 0.83-5 micron JWST transmission spectrum of the sub-Neptune TOI-421 b. This planet is unique among previously observed counterparts in its high equilibrium temperature ($T_{eq} \approx 920$) and its Sun-like host star. We find marked differences between the atmosphere of TOI-421 b and those of sub-Neptunes previously characterized with JWST, which all orbit M stars. Specifically, water features in the NIRISS/SOSS bandpass indicate a low mean molecular weight atmosphere consistent with solar metallicity, and no appreciable aerosol coverage. Hints of SO$_2$ and CO (but not CO$_2$ or CH$_4$) also exist in our NIRSpec/G395M observations, but not at sufficient signal-to-noise to draw firm conclusions. Our results support a picture in which sub-Neptunes hotter than ~850 K do not form hydrocarbon hazes due to a lack of methane to photolyze. TOI-421 b additionally fits the paradigm of the radius valley for planets orbiting FGK stars being sculpted by mass loss processes, which would leave behind primordial atmospheres overlying rock/iron interiors. Further observations of TOI-421 b and similar hot sub-Neptunes will confirm whether haze-free atmospheres and low mean molecular weights are universal characteristics of such objects., Comment: Submitted to ApJ Letters, comments welcome

Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in DNA sequencing, development of new computational and experimental approaches to prioritize genes and genetic variants, and increased global exchange of clinical and genetic data. However, more than half of individuals suspected to have a rare disease lack a genetic diagnosis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was initiated to study thousands of challenging rare disease cases and families and apply, standardize, and evaluate emerging genomics technologies and analytics to accelerate their adoption in clinical practice. Further, all data generated, currently representing ~7500 individuals from ~3000 families, is rapidly made available to researchers worldwide via the Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) to catalyze global efforts to develop approaches for genetic diagnoses in rare diseases (https://gregorconsortium.org/data). The majority of these families have undergone prior clinical genetic testing but remained unsolved, with most being exome-negative. Here, we describe the collaborative research framework, datasets, and discoveries comprising GREGoR that will provide foundational resources and substrates for the future of rare disease genomics.

In this work we build optimal experimental designs for precise estimation of the functional coefficient of a function-on-function linear regression model where both the response and the factors are continuous functions of time. After obtaining the variance-covariance matrix of the estimator of the functional coefficient which minimizes the integrated sum of square of errors, we extend the classical definition of optimal design to this estimator, and we provide the expression of the A-optimal and of the D-optimal designs. Examples of optimal designs for dynamic experimental factors are then computed through a suitable algorithm, and we discuss different scenarios in terms of the set of basis functions used for their representation. Finally, we present an example with simulated data to illustrate the feasibility of our methodology., Comment: 15 figures

The Ni-Au-Ga interdiffusion mechanisms taking place during rapid thermal annealing (RTA) under oxygen atmosphere of a Ni-Au/p-GaN contact are investigated by high-resolution transmission electron microscopy (HR-TEM) coupled to energy dispersive X-ray spectroscopy (EDX). It is shown that oxygen-assisted, Ni diffusion to the top surface of the metallic contact through the formation of a nickel oxide (NiOx) is accompanied by Au diffusion down to the GaN surface, and by Ga out-diffusion through the GaN/metal interface. Electrical characterizations of the contact by Transmission Line Method (TLM) show that an ohmic contact is obtained as soon as a thin, Au-Ga interfacial layer is formed, even after complete diffusion of Ni or NiOx to the top surface of the contact. Our results clarify that the presence of Ni or NiOx at the interface is not the main origin of the ohmic-like behavior in such contacts. Auto-cleaning of the interface during the interdiffusion process may play a role, but TEM-EDX analysis evidences that the creation of Ga vacancies associated to the formation of a Ga-Au interfacial layer is crucial for reducing the Schottky barrier height, and maximizing the amount of current flowing through the contact.

We define a normed matrix factorization category and a notion of bounding cochains for objects of this category. We classify bounding cochains up to gauge equivalence for spherical objects and use this classification to define numerical invariants. These invariants are expected to correspond under mirror symmetry to the open Gromov-Witten invariants with only boundary constraints of Lagrangian rational cohomology spheres defined by the second author and Tukachinsky. For each Delzant polytope, we construct a normed matrix factorization category. For Delzant polytopes satisfying a combinatorial relative spin condition, we construct an object of this category called the Dirac factorization. The Dirac factorization is expected to correspond under mirror symmetry to the Lagrangian submanifold given by the real locus of the toric symplectic manifold associated to the Delzant polytope. In the case of the $n$-simplex for $n$ odd, we show that the Dirac factorization is spherical, mirroring the fact that $\mathbb{R} P^n$ is a rational cohomology sphere. For $n = 1,$ we show the numerical invariants of the Dirac factorization coincide with the open Gromov-Witten invariants of $\mathbb{R} P^1 \subset \mathbb{C} P^1.$ For $n = 3$ in low degrees, computer calculations verify that the numerical invariants of the Dirac factorization coincide with the open Gromov-Witten-Welschinger invariants of $\mathbb{R}P^3 \subset \mathbb{C} P^3.$ Although $\mathbb{R} P^n$ is trivial in the Fukaya category of $\mathbb{C} P^n$ over any field of characteristic zero, the above results can be seen as a manifestation of mirror symmetry over a Novikov ring., Comment: 82 pages, 1 figure

Water has proven to be ubiquitously detected in near-infrared (NIR) transmission spectroscopy observations of hot Jupiter atmospheres, including WASP-17b. However, previous analyses of WASP-17b's atmosphere based upon Hubble Space Telescope (HST) and Spitzer data could not constrain the water abundance, finding that sub-solar, super-solar and bimodal posterior distributions were all statistically valid. In this work, we observe one transit of the hot Jupiter WASP-17b using JWST's Near Infrared Imager and Slitless Spectrograph Single Object Slitless Spectroscopy (NIRISS SOSS) mode. We analyze our data using three independent data analysis pipelines, finding excellent agreement between results. Our transmission spectrum shows multiple H$_2$O absorption features and a flatter slope towards the optical than seen in previous HST observations. We analyze our spectrum using both PICASO+Virga forward models and free retrievals. POSEIDON retrievals provide a well-constrained super-solar $\log$(H$_2$O) abundance (-2.96$^{+0.31}_{-0.24}$), breaking the degeneracy from the previous HST/Spitzer analysis. We verify our POSEIDON results with petitRADTRANS retrievals. Additionally, we constrain the abundance of $\log$(H$^-$), -10.19$^{+0.30}_{-0.23}$, finding that our model including H$^-$ is preferred over our model without H$^-$ to 5.1 $\sigma$. Furthermore, we constrain the $\log$(K) abundance (-8.07$^{+0.58}_{-0.52}$) in WASP-17b's atmosphere for the first time using space-based observations. Our abundance constraints demonstrate the power of NIRISS SOSS's increased resolution, precision, and wavelength range to improve upon previous NIR space-based results. This work is part of a series of studies by our JWST Telescope Scientist Team (JWST-TST), in which we use Guaranteed Time Observations to perform Deep Reconnaissance of Exoplanet Atmospheres through Multi-instrument Spectroscopy (DREAMS)., Comment: Accepted for Publication in the Astronomical Journal (AJ) on 19 November 2024; 37 Pages, 17 Figures, 7 Tables. This version adds 2 co-authors, updates affiliations/acknowledgements, and corrects minor typos. Zenodo link to data is at https://zenodo.org/records/14193061

This paper introduces SmartBSP, an advanced self-supervised learning framework for real-time path planning and obstacle avoidance in autonomous robotics navigating through complex environments. The proposed system integrates Proximal Policy Optimization (PPO) with Convolutional Neural Networks (CNN) and Actor-Critic architecture to process limited LIDAR inputs and compute spatial decision-making probabilities. The robot's perceptual field is discretized into a grid format, which the CNN analyzes to produce a spatial probability distribution. During the training process a nuanced cost function is minimized that accounts for path curvature, endpoint proximity, and obstacle avoidance. Simulations results in different scenarios validate the algorithm's resilience and adaptability across diverse operational scenarios. Subsequently, Real-time experiments, employing the Robot Operating System (ROS), were carried out to assess the efficacy of the proposed algorithm.

As events progress, news articles often update with new information: if we are not cautious, we risk propagating outdated facts. In this work, we hypothesize that linguistic features indicate factual fluidity, and that we can predict which facts in a news article will update using solely the text of a news article (i.e. not external resources like search engines). We test this hypothesis, first, by isolating fact-updates in large news revisions corpora. News articles may update for many reasons (e.g. factual, stylistic, narrative). We introduce the NewsEdits 2.0 taxonomy, an edit-intentions schema that separates fact updates from stylistic and narrative updates in news writing. We annotate over 9,200 pairs of sentence revisions and train high-scoring ensemble models to apply this schema. Then, taking a large dataset of silver-labeled pairs, we show that we can predict when facts will update in older article drafts with high precision. Finally, to demonstrate the usefulness of these findings, we construct a language model question asking (LLM-QA) abstention task. We wish the LLM to abstain from answering questions when information is likely to become outdated. Using our predictions, we show, LLM absention reaches near oracle levels of accuracy., Comment: 9 pages main body, 11 pages appendix

Data-Free Knowledge Distillation (DFKD) is an advanced technique that enables knowledge transfer from a teacher model to a student model without relying on original training data. While DFKD methods have achieved success on smaller datasets like CIFAR10 and CIFAR100, they encounter challenges on larger, high-resolution datasets such as ImageNet. A primary issue with previous approaches is their generation of synthetic images at high resolutions (e.g., $224 \times 224$) without leveraging information from real images, often resulting in noisy images that lack essential class-specific features in large datasets. Additionally, the computational cost of generating the extensive data needed for effective knowledge transfer can be prohibitive. In this paper, we introduce MUlti-reSolution data-freE (MUSE) to address these limitations. MUSE generates images at lower resolutions while using Class Activation Maps (CAMs) to ensure that the generated images retain critical, class-specific features. To further enhance model diversity, we propose multi-resolution generation and embedding diversity techniques that strengthen latent space representations, leading to significant performance improvements. Experimental results demonstrate that MUSE achieves state-of-the-art performance across both small- and large-scale datasets, with notable performance gains of up to two digits in nearly all ImageNet and subset experiments. Code is available at https://github.com/tmtuan1307/muse.

Incorporating artificial intelligence (AI) into digital pathology offers promising prospects for automating and enhancing tasks such as image analysis and diagnostic processes. However, the diversity of tissue samples and the necessity for meticulous image labeling often result in biased datasets, constraining the applicability of algorithms trained on them. To harness synthetic histopathological images to cope with this challenge, it is essential not only to produce photorealistic images but also to be able to exert control over the cellular characteristics they depict. Previous studies used methods to generate, from random noise, semantic masks that captured the spatial distribution of the tissue. These masks were then used as a prior for conditional generative approaches to produce photorealistic histopathological images. However, as with many other generative models, this solution exhibits mode collapse as the model fails to capture the full diversity of the underlying data distribution. In this work, we present a pipeline, coined PriorPath, that generates detailed, realistic, semantic masks derived from coarse-grained images delineating tissue regions. This approach enables control over the spatial arrangement of the generated masks and, consequently, the resulting synthetic images. We demonstrated the efficacy of our method across three cancer types, skin, prostate, and lung, showcasing PriorPath's capability to cover the semantic mask space and to provide better similarity to real masks compared to previous methods. Our approach allows for specifying desired tissue distributions and obtaining both photorealistic masks and images within a single platform, thus providing a state-of-the-art, controllable solution for generating histopathological images to facilitate AI for computational pathology.

Large Language Models (LLMs) have demonstrated impressive capabilities in generating coherent text but often struggle with grounding language and strategic dialogue. To address this gap, we focus on journalistic interviews, a domain rich in grounding communication and abundant in data. We curate a dataset of 40,000 two-person informational interviews from NPR and CNN, and reveal that LLMs are significantly less likely than human interviewers to use acknowledgements and to pivot to higher-level questions. Realizing that a fundamental deficit exists in multi-turn planning and strategic thinking, we develop a realistic simulated environment, incorporating source personas and persuasive elements, in order to facilitate the development of agents with longer-horizon rewards. Our experiments show that while source LLMs mimic human behavior in information sharing, interviewer LLMs struggle with recognizing when questions are answered and engaging persuasively, leading to suboptimal information extraction across model size and capability. These findings underscore the need for enhancing LLMs' strategic dialogue capabilities.

Understanding the complex ground state of van der Waals (vdW) magnets is essential for designing new materials and devices that leverage these platforms. Here, we investigate a two-dimensional vdW ferromagnet -- Fe$_5$GeTe$_2$-- with one of the highest reported Curie temperatures, to elucidate its magnetic excitations and charge order. Using Fe $L_3 - $edge resonant inelastic x-ray scattering, we find the dual character of magnetic excitations, consisting of a coherent magnon and a continuum, similar to what is reported for its sister compound Fe$_3$GeTe$_2$. The magnon has an energy of $\approx$ 36 meV at the maximum in-plane momentum transfer ($-$0.35 r.l.u.) allowed at Fe $L_3 - $edge. A broad and non-dispersive continuum extends up to 150 meV, 50$\%$ higher energy than in Fe$_3$GeTe$_2$. Its intensity is sinusoidally modulated along the $L$ direction, with a period matching the inter-slab distance. Our findings suggest that while the unconventional dual character of magnetic excitations is generic to ternary Fe-Ge-Te vdW magnets, the correlation length of the out-of-plane magnetic interaction increases in Fe$_5$GeTe$_2$ as compared to Fe$_3$GeTe$_2$, supporting a stronger three-dimensional character for the former. Furthermore, by investigating the $\pm$(1/3, 1/3, $L$) peaks by resonant x-ray diffraction, we conclude these to have structural origin rather than charge order -- as previously reported -- and suggest doubling of the structural unit cell along the $c-$axis., Comment: 17 pages, 3 figures

In the AdS/CFT correspondence, a subregion of the CFT allows for the recovery of a corresponding subregion of the bulk known as its entanglement wedge. In some cases, an entanglement wedge contains a locally but not globally minimal surface homologous to the CFT subregion, in which case it is said to contain a python's lunch. It has been proposed that python's lunch geometries should be modelled by tensor networks that feature projective operations where the wedge narrows. This model leads to the python's lunch (PL) conjecture, which asserts that reconstructing information from past the locally minimal surface is computationally difficult. In this work, we invoke cryptographic tools pertaining to a primitive known as the Conditional Disclosure of Secrets (CDS) to develop consequences of the projective tensor network model that can be checked directly in AdS/CFT. We argue from the tensor network picture that the mutual information between appropriate CFT subregions is lower bounded linearly by an area difference associated with the geometry of the lunch. Recalling that the mutual information is also computed by bulk extremal surfaces, this gives a checkable geometrical consequence of the tensor network model. We prove weakened versions of this geometrical statement in asymptotically AdS$_{2+1}$ spacetimes satisfying the null energy condition, and confirm it in some example geometries, supporting the tensor network model and by proxy the PL conjecture. On the other hand, we point out a tension between the PL conjecture and a plausible cryptographically inspired lower bound on the mutual information involving the complexity of reconstructing operators inside the lunch; this suggests the existence of protocols for computationally secure CDS requiring unexpectedly small entanglement., Comment: An important update to the estimated security parameter for holographic CDS is given, leading to a different conjectured lower bound than in v1. Updates to the structure of the paper reflect this change

In this work we present an a priori error analysis for solving the unsteady advection equation on cut cell meshes along a straight ramp in two dimensions. The space discretization uses a lowest order upwind-type discontinuous Galerkin scheme involving a \textit{Domain of Dependence} (DoD) stabilization to correct the update in the neighborhood of small cut cells. Thereby, it is possible to employ explicit time stepping schemes with a time step length that is independent of the size of the very small cut cells. Our error analysis is based on a general framework for error estimates for first-order linear partial differential equations that relies on consistency, boundedness, and discrete dissipation of the discrete bilinear form. We prove these properties for the space discretization involving DoD stabilization. This allows us to prove, for the fully discrete scheme, a quasi-optimal error estimate of order one half in a norm that combines the $L^\infty$-in-time $L^2$-in-space norm and a seminorm that contains velocity weighted jumps. We also provide corresponding numerical results.

Important advances have recently been made in the search for materials with complex multi-phase landscapes that host photoinduced metastable collective states with exotic functionalities. In almost all cases so far, the desired phases are accessed by exploiting light-matter interactions via the imaginary part of the dielectric function through above-bandgap or resonant mode excitation. Nonresonant Raman excitation of coherent modes has been experimentally observed and proposed for dynamic material control, but the resulting atomic excursion has been limited to perturbative levels. Here, we demonstrate that it is possible to overcome this challenge by employing nonresonant ultrashort pulses with low photon energies well below the bandgap. Using mid-infrared pulses, we induce ferroelectric reversal in lithium niobate and phase switching in tin selenide and characterize the large-amplitude mode displacements through femtosecond Raman scattering, second harmonic generation, and x-ray diffraction. This approach, validated by first-principle calculations, defines a novel method for synthesizing hidden phases with unique functional properties and manipulating complex energy landscapes at reduced energy consumption and ultrafast speeds., Comment: 5 figures

AIs can beat humans in game environments; however, how helpful those agents are to human remains understudied. We augment CICERO, a natural language agent that demonstrates superhuman performance in Diplomacy, to generate both move and message advice based on player intentions. A dozen Diplomacy games with novice and experienced players, with varying advice settings, show that some of the generated advice is beneficial. It helps novices compete with experienced players and in some instances even surpass them. The mere presence of advice can be advantageous, even if players do not follow it., Comment: 9 pages, 3 figures

Minimoons are asteroids that become temporarily captured by the Earth-Moon system. We present the discovery of 2024 PT$_5$, a minimoon discovered by the Asteroid Terrestrial-impact Last Alert System (ATLAS) Sutherland telescope on 2024 August 7. The minimoon with heliocentric semi-major axis, $a$$\sim$1.01 au, and perihelion, $q$$\sim$0.99 au, became captured by the Earth-Moon system on 2024 September 29 and left on 2024 November 25 UTC. Visible g, r, i, and Z spectrophotometry was obtained using Gemini North/Gemini Multi-Object Spectrograph (GMOS) on 2024 September 27. The color indices are g-r = 0.58$\pm$0.04, r-i = 0.29$\pm$0.04, i-Z = -0.27$\pm$0.06, and the spectrum best matches lunar rock samples followed by S-complex asteroids. Assuming an albedo of 0.21 and using our measured absolute magnitude of 28.64$\pm$0.04, 2024 PT$_5$ has a diameter of 5.4$\pm$1.2 m. We also detect variations in the lightcurve of 2024 PT$_5$ with a 0.28$\pm$0.07 magnitude amplitude and a double-peaked period of $\sim$2600$\pm$500 s. We improve the orbital solution of 2024 PT$_5$ with our astrometry and estimate the effect of radiation pressure on its deriving an area-to-mass ratio of 7.02$\pm$2.05$\times$10$^{-5}$ m$^2$/kg, implying a density of $\sim$3.9$\pm$2.1 g/cm$^3$, compatible with having a rocky composition. If we assume 2024 PT$_5$ is from the NEO population, its most likely sources are resonances in the inner Main Belt by comparing its orbit with the NEO population model, though this does not exclude a lunar origin., Comment: 23 pages, 7 figures, 3 tables, accepted for publication in The Astrophysical Journal Letters. arXiv admin note: text overlap with arXiv:2008.05384

Homophones present a significant challenge to authors in any languages due to their similarities of pronunciations but different meanings and spellings. This issue is particularly pronounced in the Khmer language, rich in homophones due to its complex structure and extensive character set. This research aims to address the difficulties faced by Khmer authors when using homophones in their writing and proposes potential solutions based on an extensive literature review and survey analysis. A survey of 108 Khmer native speakers, including students, employees, and professionals, revealed that many frequently encounter challenges with homophones in their writing, often struggling to choose the correct word based on context. The survey also highlighted the absence of effective tools to address homophone errors in Khmer, which complicates the writing process. Additionally, a review of existing studies on spelling correction in other languages, such as English, Azerbaijani, and Bangla, identified a lack of research focused specifically on homophones, particularly in the Khmer language. In summary, this research highlights the necessity for a specialized tool to address Khmer homophone errors. By bridging current gaps in research and available resources, such a tool would enhance the confidence and accuracy of Khmer authors in their writing, thereby contributing to the enrichment and preservation of the language. Continued efforts in this domain are essential for ensuring that Khmer can leverage advancements in technology and linguistics effectively.

Trapped ions provide a highly controlled platform for quantum sensors, clocks, simulators, and computers, all of which depend on cooling ions close to their motional ground state. Existing methods like Doppler, resolved sideband, and dark resonance cooling balance trade-offs between the final temperature and cooling rate. A traveling polarization gradient has been shown to cool multiple modes quickly and in parallel, but utilizing a stable polarization gradient can achieve lower ion energies, while also allowing more tailorable light-matter interactions in general. In this paper, we demonstrate cooling of a trapped ion below the Doppler limit using a phase-stable polarization gradient created using trap-integrated photonic devices. At an axial frequency of $2\pi\cdot1.45~ \rm MHz$ we achieve $\langle n \rangle = 1.3 \pm 1.1$ in $500~\mu \rm s$ and cooling rates of ${\sim}0.3 \, \rm quanta/\mu s$. We examine ion dynamics under different polarization gradient phases, detunings, and intensities, showing reasonable agreement between experimental results and a simple model. Cooling is fast and power-efficient, with improved performance compared to simulated operation under the corresponding running wave configuration., Comment: 16 pages, 11 figures

Large Language Models (LLMs) have demonstrated remarkable capabilities in conversational tasks. Embodying an LLM as a virtual human allows users to engage in face-to-face social interactions in Virtual Reality. However, the influence of person- and task-related factors in social interactions with LLM-controlled agents remains unclear. In this study, forty-six participants interacted with a virtual agent whose persona was manipulated as extravert or introvert in three different conversational tasks (small talk, knowledge test, convincing). Social-evaluation, emotional experience, and realism were assessed using ratings. Interactive engagement was measured by quantifying participants' words and conversational turns. Finally, we measured participants' willingness to ask the agent for help during the knowledge test. Our findings show that the extraverted agent was more positively evaluated, elicited a more pleasant experience and greater engagement, and was assessed as more realistic compared to the introverted agent. Whereas persona did not affect the tendency to ask for help, participants were generally more confident in the answer when they had help of the LLM. Variation of personality traits of LLM-controlled embodied virtual agents, therefore, affects social-emotional processing and behavior in virtual interactions. Embodied virtual agents allow the presentation of naturalistic social encounters in a virtual environment., Comment: 11 pages, 5 figures

Human writers plan, then write. For large language models (LLMs) to play a role in longer-form article generation, we must understand the planning steps humans make before writing. We explore one kind of planning, source-selection in news, as a case-study for evaluating plans in long-form generation. We ask: why do specific stories call for specific kinds of sources? We imagine a generative process for story writing where a source-selection schema is first selected by a journalist, and then sources are chosen based on categories in that schema. Learning the article's plan means predicting the schema initially chosen by the journalist. Working with professional journalists, we adapt five existing schemata and introduce three new ones to describe journalistic plans for the inclusion of sources in documents. Then, inspired by Bayesian latent-variable modeling, we develop metrics to select the most likely plan, or schema, underlying a story, which we use to compare schemata. We find that two schemata: stance and social affiliation best explain source plans in most documents. However, other schemata like textual entailment explain source plans in factually rich topics like "Science". Finally, we find we can predict the most suitable schema given just the article's headline with reasonable accuracy. We see this as an important case-study for human planning, and provides a framework and approach for evaluating other kinds of plans. We release a corpora, NewsSources, with annotations for 4M articles., Comment: 9 pages

The deployment of ML models on edge devices is challenged by limited computational resources and energy availability. While split computing enables the decomposition of large neural networks (NNs) and allows partial computation on both edge and cloud devices, identifying the most suitable split layer and hardware configurations is a non-trivial task. This process is in fact hindered by the large configuration space, the non-linear dependencies between software and hardware parameters, the heterogeneous hardware and energy characteristics, and the dynamic workload conditions. To overcome this challenge, we propose DynaSplit, a two-phase framework that dynamically configures parameters across both software (i.e., split layer) and hardware (e.g., accelerator usage, CPU frequency). During the Offline Phase, we solve a multi-objective optimization problem with a meta-heuristic approach to discover optimal settings. During the Online Phase, a scheduling algorithm identifies the most suitable settings for an incoming inference request and configures the system accordingly. We evaluate DynaSplit using popular pre-trained NNs on a real-world testbed. Experimental results show a reduction in energy consumption up to 72% compared to cloud-only computation, while meeting ~90% of user request's latency threshold compared to baselines.

Multi-document (MD) processing is crucial for LLMs to handle real-world tasks such as summarization and question-answering across large sets of documents. While LLMs have improved at processing long inputs, MD contexts still present challenges, such as managing inter-document dependencies, redundancy, and incoherent structures. We introduce MDCure, a scalable and effective fine-tuning pipeline to enhance the MD capabilities of LLMs without the computational cost of pre-training or reliance on human annotated data. MDCure is based on generation of high-quality synthetic MD instruction data from sets of related articles via targeted prompts. We further introduce MDCureRM, a multi-objective reward model which filters generated data based on their training utility for MD settings. With MDCure, we fine-tune a variety of LLMs, from the FlanT5, Qwen2, and LLAMA3.1 model families, up to 70B parameters in size. Extensive evaluations on a wide range of MD and long-context benchmarks spanning various tasks show MDCure consistently improves performance over pre-trained baselines and over corresponding base models by up to 75.5%. Our code, datasets, and models are available at https://github.com/yale-nlp/MDCure.

Oscillating clouds of ultralight bosons can grow around spinning black holes through superradiance, extracting energy and angular momentum, and eventually dissipating through gravitational radiation. Gravitational wave detectors like LIGO, Virgo, KAGRA, and LISA can thus probe the existence of ultralight bosons. In this study, we use fully general-relativistic solutions of the black hole-boson cloud systems to study the self-gravity effects of scalar and vector boson clouds, making only the simplifying assumption that the spacetime is axisymmetric (essentially corresponding to taking an oscillation average). We calculate the self-gravity shift in the cloud oscillation frequency, which determines the frequency evolution of the gravitational wave signal, finding that this effect can be up to twice as large in the relativistic regime compared to non-relativistic estimates. We use this to improve the superrad waveform model, and estimate that this reduces the theoretical phase error to a few cycles over the characteristic timescale of the gravitational wave emission timescale for the louder vector boson signals. We also perform an analysis of the spacetime geometry of these systems, calculating how the cloud changes the innermost stable circular orbit and light-ring around the black hole., Comment: 20 pages

Complex news events, such as natural disasters and socio-political conflicts, require swift responses from the government and society. Relying on historical events to project the future is insufficient as such events are sparse and do not cover all possible conditions and nuanced situations. Simulation of these complex events can help better prepare and reduce the negative impact. We develop a controllable complex news event simulator guided by both the event schema representing domain knowledge about the scenario and user-provided assumptions representing case-specific conditions. As event dynamics depend on the fine-grained social and cultural context, we further introduce a geo-diverse commonsense and cultural norm-aware knowledge enhancement component. To enhance the coherence of the simulation, apart from the global timeline of events, we take an agent-based approach to simulate the individual character states, plans, and actions. By incorporating the schema and cultural norms, our generated simulations achieve much higher coherence and appropriateness and are received favorably by participants from a humanitarian assistance organization., Comment: Accepted as EMNLP 2024 Demo

We prove the asymptotic stability of the high speed solitary waves to the Benjamin equation. This is done by establishing a Liouville property for the nonlinear evolution of the Benjamin equation around these solitary waves. To do this, inspired by Kenig-Martel-Robbiano 2011, we make use of the KdV limit of the Benjamin equation together with known rigidity property of the KdV flow. The main difficulties are linked to the presence of the Hilbert transform, that is a non-local operator, as well as the non-positivity of the quadratic part of the energy in the case $ \gamma<0$ which is the physical case.

We describe the cryogenic system being developed for Taurus: a super-pressure balloon-borne microwave polarimeter scheduled to fly in 2027. The Taurus cryogenic system consists of a 660L liquid helium cryostat which achieves a base temperature of <100mK with the help of a capillary-fed superfluid tank and a closed cycle dilution refrigerator. The main tank is supported with fiberglass flexures and is encased in two layers of vapor-cooled shields which allow Taurus to make full use of the extended flight time offered by the super-pressure balloon platform. The Taurus cryostat is projected to hold for over 50 days while weighing under 1000lbs. We present the design, testing, and thermal analysis of the Taurus cryogenic systems.

In twisted van der Waals materials, local atomic relaxation can significantly alter the underlying electronic structure and properties. Characterizing the lattice reconstruction and the impact of strain is essential to better understand and harness the resulting emergent electronic states. Here, we use a scanning single-electron transistor to image spatial modulations in the electronic structure of helical trilayer graphene, demonstrating relaxation into a superstructure of large domains with uniform moire periodicity. We further show that the supermoire domain size is enhanced by strain and can even be altered in subsequent measurements of the same device, while nevertheless maintaining the same local electronic properties within each domain. Finally, we observe higher conductance at the boundaries between domains, consistent with the prediction that they host counter-propagating topological edge modes. Our work confirms that lattice relaxation can produce moire-periodic order in twisted multilayers, demonstrates strain-engineering as a viable path for designing topological networks at the supermoire scale, and paves the way to direct imaging of correlation-driven topological phases and boundary modes.

This article investigates equivariant parametrized cellular cohomology, a cohomology theory introduced by Costenoble-Waner for spaces with an action by a compact Lie group $G$. The theory extends the $RO(G)$-graded cohomology of a $G$-space $B$ to a cohomology graded by $RO(\Pi B)$, the representations of the equivariant fundamental groupoid of $B$. This paper is meant to serve as a guide to this theory and contains some new computations. We explain the key ingredients for defining parametrized cellular cohomology when $G$ is a finite group, with particular attention to the case of the cyclic group $G=C_2$. We compute some examples and observe that $RO(\Pi B)$ is not always free. When $G$ is the trivial group, we explain how to identify equivariant parametrized cellular cohomology with cellular cohomology in local coefficients. Finally, we illustrate the theory with some new computations of parametrized cellular cohomology for several spaces with $G = C_2$ and $G=C_4$.

Faced with an expensive human annotation process, creators of NLP systems increasingly turn to synthetic data generation. While this method shows promise, the extent to which synthetic data can replace human annotation is poorly understood. We investigate the use of synthetic data in Fact Verification (FV) and Question Answering (QA) by studying the effects of incrementally replacing human generated data with synthetic points on eight diverse datasets. Strikingly, replacing up to 90% of the training data only marginally decreases performance, but replacing the final 10% leads to severe declines. We find that models trained on purely synthetic data can be reliably improved by including as few as 125 human generated data points. We show that matching the performance gain of just a little additional human data (only 200 points) requires an order of magnitude more synthetic data and estimate price ratios at which human annotation would be a more cost-effective solution. Our results suggest that even when human annotation at scale is infeasible, there is great value to having a small proportion of the dataset being human generated.

This paper presents a novel approach termed Layer-of-Thoughts Prompting (LoT), which utilizes constraint hierarchies to filter and refine candidate responses to a given query. By integrating these constraints, our method enables a structured retrieval process that enhances explainability and automation. Existing methods have explored various prompting techniques but often present overly generalized frameworks without delving into the nuances of prompts in multi-turn interactions. Our work addresses this gap by focusing on the hierarchical relationships among prompts. We demonstrate that the efficacy of thought hierarchy plays a critical role in developing efficient and interpretable retrieval algorithms. Leveraging Large Language Models (LLMs), LoT significantly improves the accuracy and comprehensibility of information retrieval tasks., Comment: Presented at NeLaMKRR@KR, 2024 (arXiv:2410.05339)