Search

The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time., Comment: Corresponding author: S. Doeleman

Activity and autonomous motion are fundamental aspects of many living and engineering systems. Here, the scale of biological agents covers a wide range, from nanomotors, cytoskeleton, and cells, to insects, fish, birds, and people. Inspired by biological active systems, various types of autonomous synthetic nano- and micromachines have been designed, which provide the basis for multifunctional, highly responsive, intelligent active materials. A major challenge for understanding and designing active matter is their inherent non-equilibrium nature due to persistent energy consumption, which invalidates equilibrium concepts such as free energy, detailed balance, and time-reversal symmetry. Furthermore, interactions in ensembles of active agents are often non-additive and non-reciprocal. An important aspect of biological agents is their ability to sense the environment, process this information, and adjust their motion accordingly. It is an important goal for the engineering of micro-robotic systems to achieve similar functionality. With many fundamental properties of motile active matter now reasonably well understood and under control, the ground is prepared for the study of physical aspects and mechanisms of motion in complex environments, of the behavior of systems with new physical features like chirality, of the development of novel micromachines and microbots, of the emergent collective behavior and swarming of intelligent self-propelled particles, and of particular features of microbial systems. The vast complexity of phenomena and mechanisms involved in the self-organization and dynamics of motile active matter poses major challenges, which can only be addressed by a truly interdisciplinary effort involving scientists from biology, chemistry, ecology, engineering, mathematics, and physics.

Interactions between magma oceans and overlying atmospheres on young rocky planets leads to an evolving feedback of outgassing, greenhouse forcing, and mantle melt fraction. Previous studies have predominantly focused on the solidification of oxidized Earth-similar planets, but the diversity in mean density and irradiation observed in the low-mass exoplanet census motivate exploration of strongly varying geochemical scenarios. We aim to explore how variable redox properties alter the duration of magma ocean solidification, the equilibrium thermodynamic state, melt fraction of the mantle, and atmospheric composition. We develop a 1D coupled interior-atmosphere model that can simulate the time-evolution of lava planets. This is applied across a grid of fixed redox states, orbital separations, hydrogen endowments, and C/H ratios around a Sun-like star. The composition of these atmospheres is highly variable before and during solidification. The evolutionary path of an Earth-like planet at 1 AU ranges between permanent magma ocean states and solidification within 1 Myr. Recently solidified planets typically host H2O- or H2-dominated atmospheres in the absence of escape. Orbital separation is the primary factor determining magma ocean evolution, followed by the total hydrogen endowment, mantle oxygen fugacity, and finally the planet's C/H ratio. Collisional absorption by H2 induces a greenhouse effect which can prevent or stall magma ocean solidification. Through this effect, as well as the outgassing of other volatiles, geochemical properties exert significant control over the fate of magma oceans on rocky planets., Comment: Accepted for publication in JGR: Planets

The dynamics of a quantum system coupled to a classical environment and subject to constraints that drive it out of equilibrium is described. The evolution of the system is governed by the quantum-classical Liouville equation. Rather than evaluating the evolution of the mixed quantum-classical density operator, we derive exact equations of motion for the nonequilibrium average values of a set of operators or variables, along with correlation function expressions for the dissipative coefficients that enter these equations. These equations are obtained by requiring that the exact nonequilibrium averages are equal to local nonequilibrium averages that depend on auxiliary fields whose values satisfy evolution equations obtained using projection operator methods. The results are illustrated by deriving reaction-diffusion equations coupled to fluid hydrodynamic equations for a dilute solution of quantum particles that can exist in two metastable states. Nonequilibrium steady states are discussed along with the reaction rate and diffusion correlation functions that characterize such states., Comment: 13 pages

In this paper, we explore the intersection of technology and cultural preservation by developing a self-supervised learning framework for the classification of musical symbols in historical manuscripts. Optical Music Recognition (OMR) plays a vital role in digitising and preserving musical heritage, but historical documents often lack the labelled data required by traditional methods. We overcome this challenge by training a neural-based feature extractor on unlabelled data, enabling effective classification with minimal samples. Key contributions include optimising crop preprocessing for a self-supervised Convolutional Neural Network and evaluating classification methods, including SVM, multilayer perceptrons, and prototypical networks. Our experiments yield an accuracy of 87.66\%, showcasing the potential of AI-driven methods to ensure the survival of historical music for future generations through advanced digital archiving techniques., Comment: 6 pages, The Sixth IEEE international conference on Image Processing Applications and Systems

In this work, we study the alignment (BrainScore) of large language models (LLMs) fine-tuned for moral reasoning on behavioral data and/or brain data of humans performing the same task. We also explore if fine-tuning several LLMs on the fMRI data of humans performing moral reasoning can improve the BrainScore. We fine-tune several LLMs (BERT, RoBERTa, DeBERTa) on moral reasoning behavioral data from the ETHICS benchmark [Hendrycks et al., 2020], on the moral reasoning fMRI data from Koster-Hale et al. [2013], or on both. We study both the accuracy on the ETHICS benchmark and the BrainScores between model activations and fMRI data. While larger models generally performed better on both metrics, BrainScores did not significantly improve after fine-tuning., Comment: Accepted to the 2nd Workshop on Unifying Representations in Neural Models (UniReps) at NeurIPS 2024

The field of visual and physiological optics is undergoing continuous significant advancements, driven by a deeper understanding of the human visual system and the development of cutting-edge optical technologies. This Roadmap, authored by leading experts, delves into critical areas such as corneal biomechanical properties, keratoconus, and advancements in corneal imaging and elastography. It explores the intricate structure-function relationship within the eye lens, offering new perspectives through lens models and ray tracing techniques. The document also covers advancements in retinal imaging, highlighting the current state and future directions, and the role of adaptive optics in evaluating retinal structure and function in both healthy and diseased eyes. Furthermore, it addresses the modelling of ocular surfaces using different mathematical functions and examines the factors affecting peripheral image quality in the human eye, emphasizing the importance of these aspects in visual performance. Additional topics include schematic and functional models of the human eye, the impact of optical and chromatic aberrations, and the design of contact, and intraocular lenses. Finally, the Roadmap addresses the intersection of neurosciences with vision health, presenting a comprehensive overview of current research and future trends aimed at improving visual health and optical performance. Ultimately, this Roadmap aims to serve as a valuable resource for ophthalmologists, optometrists, vision scientists, and engineers dedicated to advancing the field of visual and physiological optics., Comment: This is the pre-peer-review and pre-editing version of the article, as submitted by the author to Journal of Optics

In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits and proof-of-principle error-correction on a single logical qubit. Nevertheless, despite significant progress and excitement, the path toward a full-stack scalable technology is largely unknown. There are significant outstanding quantum hardware, fabrication, software architecture, and algorithmic challenges that are either unresolved or overlooked. These issues could seriously undermine the arrival of utility-scale quantum computers for the foreseeable future. Here, we provide a comprehensive review of these scaling challenges. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits, employing system engineering approaches, and performing distributed quantum computation within heterogeneous high-performance computing infrastructures. These opportunities for research and development could unlock certain promising applications, in particular, efficient quantum simulation/learning of quantum data generated by natural or engineered quantum systems. To estimate the true cost of such promises, we provide a detailed resource and sensitivity analysis for classically hard quantum chemistry calculations on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. Furthermore, we argue that, to tackle industry-scale classical optimization and machine learning problems in a cost-effective manner, distributed quantum-assisted probabilistic computing with custom-designed accelerators should be considered as a complementary path toward scalability., Comment: 50 pages, 42 figures

Left Ventricular Hypertrophy (LVH) is a major cardiovascular risk factor, linked to heart failure, arrhythmia, and sudden cardiac death, often resulting from chronic stress like hypertension. Electrocardiography (ECG), while varying in sensitivity, is widely accessible and cost-effective for detecting LVH-related morphological changes. This work introduces a bilateral signal warping (BSW) approach to improve ECG-based LVH diagnosis. Our method creates a library of heartbeat prototypes from patients with consistent ECG patterns. After preprocessing to eliminate baseline wander and detect R peaks, we apply BSW to cluster heartbeats, generating prototypes for both normal and LVH classes. We compare each new record to these references to support diagnosis. Experimental results show promising potential for practical application in clinical settings., Comment: 9 pages, 5 figures

Consider a graph $G$ with a path $P$ of order $n$. What conditions force $G$ to also have a long induced path? As complete bipartite graphs have long paths but no long induced paths, a natural restriction is to forbid some fixed complete bipartite graph $K_{t,t}$ as a subgraph. In this case we show that $G$ has an induced path of order $(\log \log n)^{1/5-o(1)}$. This is an exponential improvement over a result of Galvin, Rival, and Sands (1982) and comes close to a recent upper bound of order $O((\log \log n)^2)$. Another way to approach this problem is by viewing $G$ as an ordered graph (where the vertices are ordered according to their position on the path $P$). From this point of view it is most natural to consider which ordered subgraphs need to be forbidden in order to force the existence of a long induced path. Focusing on the exclusion of ordered matchings, we improve or recover a number of existing results with much simpler proofs, in a unified way. We also show that if some forbidden ordered subgraph forces the existence of a long induced path in $G$, then this induced path has size at least $\Omega((\log \log \log n)^{1/3})$, and can be chosen to be increasing with respect to $P$., Comment: 26 pages, 8 figures. Comments welcome !

Given a normalized state-vector $\psi $, we define the conditional expectation $\mathbb{E }_{\psi } (A | B ) $ of a Hermitian operator $A $ with respect to a strongly commuting family of self-adjoint operators $B $ as the best approximation, in the operator mean square norm associated to $\psi $, of $A $ by a real-valued function of $B . $ A fundamental example is the conditional expectation of the momentum operator $P $ given the position operator $X $, which is found to be the Bohm momentum. After developing the Bohm theory from this point of view we treat conditional expectations with respect to general $B $, which we apply to non-relativistic spin 1/2-particles. We derive the dynamics of the conditional expectations of momentum and spin with respect to position and the third spin component. These dynamics can be interpreted in terms of classical continuum mechanics as a two-component fluid whose components carry intrinsic angular momentum. Interpreting the joint spectrum of the conditioning operators as a space of beables, we can introduce a classical-stochastic particle dynamics on this space which is compatible with the time-evolution of the Born probability, by combining the de Broglie-Bohm guidance condition with a Markov jump process, following an idea of J. Bell. This results in a new Bohm-type model for particles with spin. A basic problem is that such auxiliary particle dynamics are far from unique. We finally examine the relation of our conditional expectations with the conditional expectations of the theory of $C^* $-algebras and, as an application, derive a general evolution equation for conditional expectations for operators acting on finite dimensional Hilbert spaces. Two appendices re-interpret the classical Bohm model as an integrable constrained Hamiltonian system, and provide the details of the two-fluid interpretation.

Ultrasound (US)-guided needle insertion is widely employed in percutaneous interventions. However, providing feedback on the needle tip position via US image presents challenges due to noise, artifacts, and the thin imaging plane of US, which degrades needle features and leads to intermittent tip visibility. In this paper, a Mamba-based US needle tracker MambaXCTrack utilizing structured state space models cross-correlation (SSMX-Corr) and implicit motion prompt is proposed, which is the first application of Mamba in US needle tracking. The SSMX-Corr enhances cross-correlation by long-range modeling and global searching of distant semantic features between template and search maps, benefiting the tracking under noise and artifacts by implicitly learning potential distant semantic cues. By combining with cross-map interleaved scan (CIS), local pixel-wise interaction with positional inductive bias can also be introduced to SSMX-Corr. The implicit low-level motion descriptor is proposed as a non-visual prompt to enhance tracking robustness, addressing the intermittent tip visibility problem. Extensive experiments on a dataset with motorized needle insertion in both phantom and tissue samples demonstrate that the proposed tracker outperforms other state-of-the-art trackers while ablation studies further highlight the effectiveness of each proposed tracking module., Comment: This work has been submitted to the IEEE for possible publication

We prove that the transitive permutation group 17T7, isomorphic to a split extension of $C_2$ by $\mathrm{PSL}_2(\mathbb{F}_{16})$, is a Galois group over the rationals. The group arises from the field of definition of the 2-torsion on an abelian fourfold with real multiplication defined over a real quadratic field. We find such fourfolds using Hilbert modular forms. Finally, building upon work of Demb\'el\'e, we show how to conjecturally reconstruct a period matrix for an abelian variety attached to a Hilbert modular form; we then use this to exhibit an explicit degree 17 polynomial with Galois group 17T7., Comment: 23 pages, comments welcome

Droplet-based microfluidic devices have substantial promise as cost-effective alternatives to current assessment tools in biological research. Moreover, machine learning models that leverage tabular data, including input design parameters and their corresponding efficiency outputs, are increasingly utilised to automate the design process of these devices and to predict their performance. However, these models fail to fully leverage the data presented in the tables, neglecting crucial contextual information, including column headings and their associated descriptions. This study presents MicroFluidic-LLMs, a framework designed for processing and feature extraction, which effectively captures contextual information from tabular data formats. MicroFluidic-LLMs overcomes processing challenges by transforming the content into a linguistic format and leveraging pre-trained large language models (LLMs) for analysis. We evaluate our MicroFluidic-LLMs framework on 11 prediction tasks, covering aspects such as geometry, flow conditions, regimes, and performance, utilising a publicly available dataset on flow-focusing droplet microfluidics. We demonstrate that our MicroFluidic-LLMs framework can empower deep neural network models to be highly effective and straightforward while minimising the need for extensive data preprocessing. Moreover, the exceptional performance of deep neural network models, particularly when combined with advanced natural language processing models such as DistilBERT and GPT-2, reduces the mean absolute error in the droplet diameter and generation rate by nearly 5- and 7-fold, respectively, and enhances the regime classification accuracy by over 4%, compared with the performance reported in a previous study. This study lays the foundation for the huge potential applications of LLMs and machine learning in a wider spectrum of microfluidic applications.

This paper views hiring as a contextual bandit problem: to find the best workers over time, firms must balance exploitation (selecting from groups with proven track records) with exploration (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on supervised learning approaches, are designed solely for exploitation. Instead, we build a resume screening algorithm that values exploration by evaluating candidates according to their statistical upside potential. Using data from professional services recruiting within a Fortune 500 firm, we show that this approach improves the quality (as measured by eventual hiring rates) of candidates selected for an interview, while also increasing demographic diversity, relative to the firm's existing practices. The same is not true for traditional supervised learning based algorithms, which improve hiring rates but select far fewer Black and Hispanic applicants. Together, our results highlight the importance of incorporating exploration in developing decision-making algorithms that are potentially both more efficient and equitable.

Our understanding of the process of terrestrial planet formation has grown markedly over the past 20 years, yet key questions remain. This review begins by first addressing the critical, earliest stage of dust coagulation and concentration. While classic studies revealed how objects that grow to $\sim$meter sizes are rapidly removed from protoplanetary disks via orbital decay (seemingly precluding growth to larger sizes), this chapter addresses how this is resolved in contemporary, streaming instability models that favor rapid planetesimal formation via gravitational collapse of solids in over-dense regions. Once formed, planetesimals grow into Mars-Earth-sized planetary embryos by a combination of pebble- and planetesimal accretion within the lifetime of the nebular disk. After the disk dissipates, these embryos typically experience a series of late giant impacts en route to attaining their final architectures. This review also highlights three different inner Solar System formation models that can match a number of empirical constraints, and also reviews ways that one or more might be ruled out in favor of another in the near future. These include (1) the Grand Tack, (2) the Early Instability and (3) Planet Formation from Rings. Additionally, this chapter discusses formation models for the closest known analogs to our own terrestrial planets: super-Earths and terrestrial exoplanets in systems also hosting gas giants. Finally, this review lays out a chain of events that may explain why the Solar System looks different than more than 99% of exoplanet systems., Comment: To be published in: Handbook of Exoplanets, 2nd Edition, Hans Deeg and Juan Antonio Belmonte (Eds. in Chief), Springer International Publishing AG, part of Springer Nature. 75 pages, 9 figures. This is an update of arXiv:1803.08830

The origin of the tight scaling relation between the mass of supermassive black holes (SMBHs; $M_{\rm BH}$) and their host-galaxy properties remains unclear. Active galactic nuclei (AGNs) probe phases of ongoing SMBH growth and offer the only opportunity to measure $M_{\rm BH}$ beyond the local Universe. However, determining AGN host galaxy stellar velocity dispersion $\sigma_\star$, and their galaxy dynamical masses $M_{\rm dyn}$, is complicated by AGN contamination, aperture effects and different host galaxy morphologies. We select a sample of AGNs for which $M_{\rm BH}$ has been independently determined to high accuracy by state-of-the-art techniques: dynamical modeling of the reverberation signal and spatially resolving the broad-line region with VLTI/GRAVITY. Using IFU observations, we spatially map the host galaxy stellar kinematics across the galaxy and bulge effective radii. We find that that the dynamically hot component of galaxy disks correlates with $M_{\rm BH}$; however, the correlations are tightest for aperture-integrated $\sigma_\star$ measured across the bulge. Accounting for the different $M_{\rm BH}$ distributions, we demonstrate - for the first time - that AGNs follow the same $M_{\rm BH}$-$\sigma_\star$ and $M_{\rm BH}$-$M_{\rm bulge, dyn}$ relations as quiescent galaxies. We confirm that the classical approach of determining the virial factor as sample-average, yielding ${\rm log }f= 0.65 \pm 0.18$, is consistent with the average $f$ from individual measurements. The similarity between the underlying scaling relations of AGNs and quiescent galaxies implies that the current AGN phase is too short to have altered BH masses on a population level. These results strengthen the local calibration of $f$ for measuring single-epoch $M_{\rm BH}$ in the distant Universe., Comment: Submitted to ApJ. 40 pages, 19 figures, 6 tables. Reuploaded after acceptance for publication

We present the detection of directional muon beams produced using a PW laser at the Lawrence Berkeley National Laboratory. The muon source is a multi-GeV electron beam generated in a 30 cm laser plasma accelerator interacting with a high-Z converter target. The GeV photons resulting from the interaction are converted into a high-flux, directional muon beam via pair production. By employing scintillators to capture delayed events, we were able to identify the produced muons and characterize the source. Using theoretical knowledge of the muon production process combined with simulations that show outstanding agreement with the experiments, we demonstrate that the multi-GeV electron beams produce muon beams with GeV energies and fluxes, at a few meters from the source, up to 4 orders of magnitude higher than cosmic ray muons. Laser-plasma-accelerator-based muon sources can therefore enhance muon imaging applications thanks to their compactness, directionality, and high fluxes which reduce the exposure time by orders of magnitude compared to cosmic ray muons. Using the Geant4-based simulation code we developed to gain insight into the experimental results, we can design future experiments and applications based on LPA-generated muons.

We introduce GamePlot, an LLM-powered assistant that supports game designers in crafting immersive narratives for turn-based games, and allows them to test these games through a collaborative game play and refine the plot throughout the process. Our user study with 14 game designers shows high levels of both satisfaction with the generated game plots and sense of ownership over the narratives, but also reconfirms that LLM are limited in their ability to generate complex and truly innovative content. We also show that diverse user populations have different expectations from AI assistants, and encourage researchers to study how tailoring assistants to diverse user groups could potentially lead to increased job satisfaction and greater creativity and innovation over time.

In this paper, we investigate the existence of parameterized algorithms running in subexponential time for two fundamental cycle-hitting problems: Feedback Vertex Set (FVS) and Triangle Hitting (TH). We focus on the class of pseudo-disk graphs, which forms a common generalization of several graph classes where such results exist, like disk graphs and square graphs. In these graphs, we show that TH can be solved in time $2^{O(k^{3/4}\log k)}n^{O(1)}$, and given a geometric representation FVS can be solved in time $2^{O(k^{6/7}\log k)}n^{O(1)}$., Comment: Presented at the conference WG 2024

In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National Science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster collaboration and build the infrastructure required to develop a diverse and quantum-ready workforce. The workshop discussions centered around how a center could uniquely address gaps in public, K-12, and undergraduate quantum information science and engineering (QISE) education. Specifically, the community identified activities that, through a center, could lead to an increase in student awareness of quantum careers, boost the number of educators trained in quantum-related subjects, strengthen pathways into quantum careers, enhance the understanding of the U.S. quantum workforce, and elevate public engagement with QISE. Core proposed activities for the center include professional development for educators, coordinated curriculum development and curation, expanded access to educational laboratory equipment, robust evaluation and assessment practices, network building, and enhanced public engagement with quantum science.

We use JWST/NIRISS slitless spectroscopy from the Next Generation Deep Extragalactic Exploratory Public (NGDEEP) Survey to investigate the physical condition of star-forming galaxies at $1.7 < z < 3.4$. At these redshifts, the deep NGDEEP NIRISS slitless spectroscopy covers the [O II]$\lambda\lambda$3726,3729, [O III]$\lambda\lambda$4959,5007, H$\beta$ and H$\alpha$ emission features for galaxies with stellar masses $\log(\mathrm{M_\ast/M_\odot}) \gtrsim 7$, nearly a factor of a hundred lower than previous studies. We focus on the [O III]/[O II] (O$_{32}$) ratio which is primarily sensitive to the ionization state and with a secondary dependence on the gas-phase metallicity of the interstellar medium. We find significant ($\gtrsim5\sigma$) correlations between the O$_{32}$ ratio and galaxy properties as O$_{32}$ increases with decreasing stellar mass, decreasing star formation rate (SFR), increasing specific SFR (sSFR$\equiv \mathrm{SFR}/M_*$), and increasing equivalent width (EW) of H$\beta$ and H$\alpha$. These trends suggest a tight connection between the ionization parameter and these galaxy properties. Galaxies at $z\sim2-3$ exhibit a higher O$_{32}$ than local normal galaxies with the same stellar masses and SFRs, indicating that they have a higher ionization parameter and lower metallicity than local normal galaxies. In addition, we observe an evolutionary trend in the O$_{32}$ -- EW(H$\beta$) relation from $z\sim0$ and $z\gtrsim5$, such that higher redshift galaxies have higher EW(H$\beta$) and higher O$_{32}$ at fixed EW. We argue that both the enhanced recent star formation activity and the higher star formation surface density may contribute to the increase in O$_{32}$ and the ionization parameter., Comment: 27 pages, 14 figures

The C-19 star stream has the abundance characteristics of an unusually metal poor globular cluster but kinematically is uncharacteristically hot and wide for a cluster stream, having a line of sight velocity dispersion of 6 \kms\ and a 1-sigma width of 240 pc. We show that the tidal dissolution of an old, lower mass, globular cluster in a CDM galactic halo naturally creates a hot, wide stream currently near orbital apocenter. More generally, simulations show that hot streams, which are all near their orbital apocenter, become thin, cool streams near pericenter. Furthermore, the wide streams from a population of dissolved clusters in the simulations have a mean galactocentric radial velocity dispersion of 7.8$\pm$1.0 \kms\ in a CDM cosmology but only 4.1$\pm$1.6 \kms\ in a WDM (5.5 keV) simulation. A detailed C-19 model in a simplified Milky Way halo potential with a CDM subhalo population provides a lower bound to stream heating, finding that the stream develops a line of sight velocity dispersion of 4.1$\pm$1.1 \kms, whereas WDM (5.5 keV) subhalos give 3.1$\pm$0.1\kms. Known dwarf galaxies alone provide negligible heating. There are five other currently known streams wider than 200 pc that contain a globular cluster, all near their orbital apocenter., Comment: AAS submitted

Multi-object 3D Grounding involves locating 3D boxes based on a given query phrase from a point cloud. It is a challenging and significant task with numerous applications in visual understanding, human-computer interaction, and robotics. To tackle this challenge, we introduce D-LISA, a two-stage approach incorporating three innovations. First, a dynamic vision module that enables a variable and learnable number of box proposals. Second, a dynamic camera positioning that extracts features for each proposal. Third, a language-informed spatial attention module that better reasons over the proposals to output the final prediction. Empirically, experiments show that our method outperforms the state-of-the-art methods on multi-object 3D grounding by 12.8% (absolute) and is competitive in single-object 3D grounding., Comment: NeurIPS 2024

In Deep Reinforcement Learning (RL), it is a challenge to learn representations that do not exhibit catastrophic forgetting or interference in non-stationary environments. Successor Features (SFs) offer a potential solution to this challenge. However, canonical techniques for learning SFs from pixel-level observations often lead to representation collapse, wherein representations degenerate and fail to capture meaningful variations in the data. More recent methods for learning SFs can avoid representation collapse, but they often involve complex losses and multiple learning phases, reducing their efficiency. We introduce a novel, simple method for learning SFs directly from pixels. Our approach uses a combination of a Temporal-difference (TD) loss and a reward prediction loss, which together capture the basic mathematical definition of SFs. We show that our approach matches or outperforms existing SF learning techniques in both 2D (Minigrid), 3D (Miniworld) mazes and Mujoco, for both single and continual learning scenarios. As well, our technique is efficient, and can reach higher levels of performance in less time than other approaches. Our work provides a new, streamlined technique for learning SFs directly from pixel observations, with no pretraining required., Comment: Main Paper: 10 pages and 8 figures. Accepted at Neural Information Processing Systems (NeurIPS) 2024

We propose a novel approach that uses large language models (LLMs) to generate persona-driven conversations between Players and Non-Player Characters (NPC) in games. Showcasing the application of our methodology, we introduce the Minecraft Persona-driven Dialogue dataset (MCPDial). Starting with a small seed of expert-written conversations, we employ our method to generate hundreds of additional conversations. Each conversation in the dataset includes rich character descriptions of the player and NPC. The conversations are long, allowing for in-depth and extensive interactions between the player and NPC. MCPDial extends beyond basic conversations by incorporating canonical function calls (e.g. "Call find a resource on iron ore") between the utterances. Finally, we conduct a qualitative analysis of the dataset to assess its quality and characteristics.

When conducting literature reviews, scientists often create literature review tables - tables whose rows are publications and whose columns constitute a schema, a set of aspects used to compare and contrast the papers. Can we automatically generate these tables using language models (LMs)? In this work, we introduce a framework that leverages LMs to perform this task by decomposing it into separate schema and value generation steps. To enable experimentation, we address two main challenges: First, we overcome a lack of high-quality datasets to benchmark table generation by curating and releasing arxivDIGESTables, a new dataset of 2,228 literature review tables extracted from ArXiv papers that synthesize a total of 7,542 research papers. Second, to support scalable evaluation of model generations against human-authored reference tables, we develop DecontextEval, an automatic evaluation method that aligns elements of tables with the same underlying aspects despite differing surface forms. Given these tools, we evaluate LMs' abilities to reconstruct reference tables, finding this task benefits from additional context to ground the generation (e.g. table captions, in-text references). Finally, through a human evaluation study we find that even when LMs fail to fully reconstruct a reference table, their generated novel aspects can still be useful., Comment: EMNLP 2024, 21 pages, 8 figures, 10 tables

Current methods for microplastic identification in water samples are costly and require expert analysis. Here, we propose a deep learning segmentation model to automatically identify microplastics in microscopic images. We labeled images of microplastic from the Moore Institute for Plastic Pollution Research and employ a Generative Adversarial Network (GAN) to supplement and generate diverse training data. To verify the validity of the generated data, we conducted a reader study where an expert was able to discern the generated microplastic from real microplastic at a rate of 68 percent. Our segmentation model trained on the combined data achieved an F1-Score of 0.91 on a diverse dataset, compared to the model without generated data's 0.82. With our findings we aim to enhance the ability of both experts and citizens to detect microplastic across diverse ecological contexts, thereby improving the cost and accessibility of microplastic analysis., Comment: 6 pages one figure

We report a proof-of-principle realisation of a decoy-state BB84 QKD protocol with phase encoding over a record-breaking 17 km of MMF at a rate of 193 kbits/s, as well as over 1 Mbit/s at a distance of 1 km. These results suggest that QKD can be deployed over MMF in metropolitan-scale telecommunication connections. Such MMF metropolitan networks are ubiquitous - thus this advance could pave the way to wide scale metropolitan deployment. We also assess the advantages of adapting the OM3 channel using mode-matching photonic lanterns on the QBER, signal gain, and key rate and compare different encoding techniques in light of MMF propagation effects. This work confirms the suitability of current QKD technology for use in existing MMF links, unlocking new opportunities for quantum applications using legacy fibre., Comment: 9 pages, 6 figures, published in Optica Quantum

We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered $\sim 14\%$ of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz where we assume the GW emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy $1 \times 10^{-5} M_{\odot} c^2$ and luminosity $4 \times 10^{-5} M_{\odot} c^2/\text{s}$ for a source emitting at 50 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as $1.04$, at frequencies above $1200$ Hz, surpassing results from SN 2019ejj., Comment: Main paper: 6 pages, 4 figures and 1 table. Total with appendices: 20 pages, 4 figures, and 1 table

This work is the first computational study of proppant leak-off through coal cleats that accounts for proppant retention in cleats, occlusion formation at cleat entrances, the resulting control of fluid leak-off, and the influence of realistic cleat roughness on these factors. Suspensions are simulated with a coupled lattice Boltzmann method-discrete element method, which explicitly models all physics, including shear-thinning fluid rheology. Firstly, using a simplified computational geometry, it is demonstrated that leak-off and mounding are both minimised when proppant invades and is retained in the cleat. This occurs most effectively with wide proppant size distributions, such as 100/635 mesh. However, when the proppant is larger than the cleat aperture, occlusions form at the cleat entrance, which can lead to significant mounding; this is observed for 100 mesh and 40/70 mesh. These findings are commensurate with an existing benchmark experiment. The present study additionally demonstrates that mounding is significantly reduced for shear-thinning fluids compared to Newtonian fluids. Simulations are then conducted with rough cleats in a realistic fracture channel. Leak-off is smallest for high cleat roughness and when cleats are narrower than a critical width, while proppant retention is largest at the same critical width but only above a certain roughness. Mounding is primarily dependent on the width, as opposed to the roughness. These results are presented as high-fidelity maps which can be directly incorporated into hydraulic fracturing simulations for improved predictions of fluid leak-off and propped reservoir volumes, and which can be tailored for different treatment and reservoir conditions.

For the 3D localization problem using point spread function (PSF) engineering, we propose a novel enhancement of our previously introduced localization neural network, LocNet. The improved network is a physics-informed neural network (PINN) that we call PiLocNet. Previous works on the localization problem may be categorized separately into model-based optimization and neural network approaches. Our PiLocNet combines the unique strengths of both approaches by incorporating forward-model-based information into the network via a data-fitting loss term that constrains the neural network to yield results that are physically sensible. We additionally incorporate certain regularization terms from the variational method, which further improves the robustness of the network in the presence of image noise, as we show for the Poisson and Gaussian noise models. This framework accords interpretability to the neural network, and the results we obtain show its superiority. Although the paper focuses on the use of single-lobe rotating PSF to encode the full 3D source location, we expect the method to be widely applicable to other PSFs and imaging problems that are constrained by known forward processes., Comment: 25 pages, 4 figures

Accurate characterization of quantum yield is crucial to the reconstruction of energy depositions in silicon at the eV scale. This work presents a new method for experimentally calculating quantum yield using vacuum UV-sensitive silicon photomultipliers (SiPMs), which can be used to determine the probabilities that a UV photon absorbed in a silicon crystal will produce one, two, or three electron-hole pairs. Results are presented which fully constrain the distribution at photon energies up to 7.75eV. This method works by exploiting the saturation of photon detection efficiency which occurs when these devices are biased sufficiently high above their avalanche breakdown voltage. The measured quantum yield values are lower than those that have been previously reported by experimental data and modelling -- this is expected to impact the sensitivity of experiments searching for light dark matter through direct detection in semiconductors, and should also be taken into account when characterizing the performance of UV photodetectors with high quantum efficiency. Measurements have been taken using a Hamamatsu VUV4 and an FBK VUV-HD3 device, showing good agreements between devices, and at a range of temperatures from 163-233K. The validity of the method is assessed using supplementary measurements of absolute photon detection efficiency, and an additional novel method of measuring average quantum yield using DC current-voltage measurements of SiPMs is presented and used for corroboration., Comment: 13 pages, 11 figures

Most existing super-resolution methods and datasets have been developed to improve the image quality in well-lighted conditions. However, these methods do not work well in real-world low-light conditions as the images captured in such conditions lose most important information and contain significant unknown noises. To solve this problem, we propose a SRRIIE dataset with an efficient conditional diffusion probabilistic models-based method. The proposed dataset contains 4800 paired low-high quality images. To ensure that the dataset are able to model the real-world image degradation in low-illumination environments, we capture images using an ILDC camera and an optical zoom lens with exposure levels ranging from -6 EV to 0 EV and ISO levels ranging from 50 to 12800. We comprehensively evaluate with various reconstruction and perceptual metrics and demonstrate the practicabilities of the SRRIIE dataset for deep learning-based methods. We show that most existing methods are less effective in preserving the structures and sharpness of restored images from complicated noises. To overcome this problem, we revise the condition for Raw sensor data and propose a novel time-melding condition for diffusion probabilistic model. Comprehensive quantitative and qualitative experimental results on the real-world benchmark datasets demonstrate the feasibility and effectivenesses of the proposed conditional diffusion probabilistic model on Raw sensor data. Code and dataset will be available at https://github.com/Yaofang-Liu/Super-Resolving, Comment: Code and dataset at https://github.com/Yaofang-Liu/Super-Resolving

Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for optimization, but presents novel challenges due to the variety of hardware platforms, workload characteristics, and system-level interactions. This paper introduces MLPerf Power, a comprehensive benchmarking methodology with capabilities to evaluate the energy efficiency of ML systems at power levels ranging from microwatts to megawatts. Developed by a consortium of industry professionals from more than 20 organizations, MLPerf Power establishes rules and best practices to ensure comparability across diverse architectures. We use representative workloads from the MLPerf benchmark suite to collect 1,841 reproducible measurements from 60 systems across the entire range of ML deployment scales. Our analysis reveals trade-offs between performance, complexity, and energy efficiency across this wide range of systems, providing actionable insights for designing optimized ML solutions from the smallest edge devices to the largest cloud infrastructures. This work emphasizes the importance of energy efficiency as a key metric in the evaluation and comparison of the ML system, laying the foundation for future research in this critical area. We discuss the implications for developing sustainable AI solutions and standardizing energy efficiency benchmarking for ML systems., Comment: 14 pages, 11 figures, 1 table

Understanding the spatial distribution of palms within tropical forests is essential for effective ecological monitoring, conservation strategies, and the sustainable integration of natural forest products into local and global supply chains. However, the analysis of remotely sensed data in these environments faces significant challenges, such as overlapping palm and tree crowns, uneven shading across the canopy surface, and the heterogeneous nature of the forest landscapes, which often affect the performance of palm detection and segmentation algorithms. To overcome these issues, we introduce PalmDSNet, a deep learning framework for real-time detection, segmentation, and counting of canopy palms. Additionally, we employ a bimodal reproduction algorithm that simulates palm spatial propagation to further enhance the understanding of these point patterns using PalmDSNet's results. We used UAV-captured imagery to create orthomosaics from 21 sites across western Ecuadorian tropical forests, covering a gradient from the everwet Choc\'o forests near Colombia to the drier forests of southwestern Ecuador. Expert annotations were used to create a comprehensive dataset, including 7,356 bounding boxes on image patches and 7,603 palm centers across five orthomosaics, encompassing a total area of 449 hectares. By combining PalmDSNet with the bimodal reproduction algorithm, which optimizes parameters for both local and global spatial variability, we effectively simulate the spatial distribution of palms in diverse and dense tropical environments, validating its utility for advanced applications in tropical forest monitoring and remote sensing analysis., Comment: 25 pages, 8 figures, 5 tables

The recently discovered Pa 30 nebula, the putative type Iax supernova remnant associated with the historical supernova of 1181 AD, shows puzzling characteristics that make it unique among known supernova remnants. In particular, Pa 30 exhibits a complex morphology, with a unique radial and filamentary structure, and it hosts a hot stellar remnant at its center, which displays oxygen-dominated, ultra-fast winds. Because of the surviving stellar remnant and the lack of hydrogen and helium in its filaments, it has been suggested that Pa 30 is the product of a failed thermonuclear explosion in a near- or super-Chandrasekhar white dwarf, which created a sub-luminous transient, a rare sub-type of the Ia class of supernovae called type Iax. We here present a detailed study of the 3D structure and velocities of a full radial section of the remnant. The Integral Field Unit (IFU) observations, obtained with the new red channel of the Keck Cosmic Web Imager spectrograph, reveal that the ejecta are consistent with being ballistic, with velocities close to the free-expansion velocity. Additionally, we detect a large cavity inside the supernova remnant and a sharp inner edge to the filamentary structure, which coincides with the outer edge of a bright ring detected in infrared images. Finally, we detect a strong asymmetry in the amount of ejecta along the line of sight, which might hint to an asymmetric explosion. Our analysis provides strong confirmation that the explosion originated from SN 1181., Comment: 13 pages, 8 figures. Accepted for publication in ApJL

The electrical response of ferroelectric domain walls is often influenced by their geometry underneath the sample surface. Tomographic imaging in these material systems has therefore become increasingly important for its ability to correlate the surface-level functional response with subsurface domain microstructure. In this context, AFM-based tomography emerges as a compelling choice because of its simplicity, high resolution and robust contrast mechanism. However, to date, the technique has been implemented in a limited number of ferroelectric materials, typically to depths of a few hundred nanometers or on relatively soft materials, resulting in an unclear understanding of its capabilities and limitations. In this work, AFM tomography is carried out in YbMnO3, mapping its complex domain microstructure up to a depth of around 1.8 um along with its current pathways. A model is presented, describing the impact of interconnected domain walls within the network, which act as current dividers and codetermine how currents distribute. Finally, challenges such as tip-blunting and subsurface amorphisation are identified through TEM studies, and strategies to address them are also put forward. This study highlights the potential of AFM tomography and could spur interest within the ferroics community for its use in the investigation of similar material systems.

The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts $\leq$ 1 s) we derive 50\% (90\%) upper limits of $10^{48}$ ($10^{49}$) erg for GWs at 300 Hz and $10^{49}$ ($10^{50}$) erg at 2 kHz, and constrain the GW-to-radio energy ratio to $\leq 10^{14} - 10^{16}$. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs., Comment: 15 pages of text including references, 4 figures, 5 tables

Text-to-visualization (text-to-vis) models have become valuable tools in the era of big data, enabling users to generate data visualizations and make informed decisions through natural language queries (NLQs). Despite their widespread application, the security vulnerabilities of these models have been largely overlooked. To address this gap, we propose VisPoison, a novel framework designed to identify these vulnerabilities of current text-to-vis models systematically. VisPoison introduces two types of triggers that activate three distinct backdoor attacks, potentially leading to data exposure, misleading visualizations, or denial-of-service (DoS) incidents. The framework features both proactive and passive attack mechanisms: proactive attacks leverage rare-word triggers to access confidential data, while passive attacks, triggered unintentionally by users, exploit a first-word trigger method, causing errors or DoS events in visualizations. Through extensive experiments on both trainable and in-context learning (ICL)-based text-to-vis models, \textit{VisPoison} achieves attack success rates of over 90\%, highlighting the security problem of current text-to-vis models. Additionally, we explore two types of defense mechanisms against these attacks, but the results show that existing countermeasures are insufficient, underscoring the pressing need for more robust security solutions in text-to-vis systems., Comment: 11 pages, 4 figures

The invention of the laser unleashed the potential of optical metrology, leading to numerous advancements in modern science and technology. This reliance on lasers, however, also sets a bottleneck for precision optical metrology which is complicated by sophisticated photonic infrastructure required for delicate laser-wave control, leading to limited metrology performance and significant system complexity. Here we make a key step towards resolving this challenge, by demonstrating a Pockels laser with multi-functional capability that advances the optical metrology to a new level. The chip-scale laser exhibits a narrow intrinsic linewidth down to 167 Hz and a broad mode-hop-free tuning range up to 24 GHz. In particular, it offers an unprecedented frequency chirping rate up to 20 EHz/s, and an enormous modulation bandwidth >10 GHz, both orders of magnitude larger than any existing lasers. With this laser, we are able to successfully achieve velocimetry of 40 m/s at a short distance of 0.4 m, with a measurable velocity up to the first cosmic velocity at 1 m away, that is inaccessible by conventional ranging approaches, and distance metrology with a ranging resolution of <2 cm. Moreover, for the first time to the best of our knowledge, we are able to realize a dramatically simplified architecture for laser frequency stabilization, by direct locking the laser to an external reference gas cell without any extra external light control. We successfully achieve a long-term laser stability with a frequency fluctuation of only $\pm$ 6.5 MHz over 60 minutes. The demonstrated Pockels laser combines elegantly high laser coherence with ultrafast frequency reconfigurability and superior multifunctional capability that we envision to have profound impacts on many areas including communication, sensing, autonomous driving, quantum information processing, and beyond., Comment: 10 pages, 4 figures

JWST spectroscopy has discovered a population of $z \gtrsim 3.5$ galaxies with broad Balmer emission lines, and narrow forbidden lines, that are consistent with hosting active galactic nuclei (AGN). Many of these systems, now known as ``little red dots" (LRDs), are compact and have unique colors that are very red in the optical/near-infrared and blue in the ultraviolet. The relative contribution of galaxy starlight and AGN to these systems remains uncertain, especially for the galaxies with unusual blue+red spectral energy distributions. In this work, we use Balmer decrements to measure the independent dust attenuation of the broad and narrow emission-line components of a sample of 29 broad-line AGN identified from three public JWST spectroscopy surveys: CEERS, JADES, and RUBIES. Stacking the narrow components from the spectra of 25 sources with broad H$\rm{\alpha}$ and no broad H$\rm{\beta}$ results in a median narrow H$\rm{\alpha}$/H$\rm{\beta}$ = $2.47^{+0.05}_{-0.05}$ (consistent with $A_{v} = 0$) and broad H$\rm{\alpha}$/H$\rm{\beta}$ $> 8.85$ ($A_{v} > 3.63$). The narrow and broad Balmer decrements imply little-to-no attenuation of the narrow emission lines, which are consistent with being powered by star formation and located on larger physical scales. Meanwhile, the lower limit in broad H$\rm{\alpha}$/H$\rm{\beta}$ decrement, with broad H$\rm{\beta}$ undetected in the stacked spectrum of 25 broad-H$\rm{\alpha}$ AGN, implies significant dust attenuation of the broad-line emitting region that is presumably associated with the central AGN. Our results indicate that these systems, on average, are consistent with heavily dust-attenuated AGN powering the red parts of their SED while their blue UV emission is powered by unattenuated star formation in the host galaxy., Comment: 4 figures, 3 tables

We performed a pump-probe experiment on the chiral magnet Cu$_2$OSeO$_3$ to study the relaxation dynamics of its non-collinear magnetic orders, employing a millisecond magnetic field pulse as the pump and resonant elastic x-ray scattering as the probe. Our findings reveal that the system requires $\sim$0.2 s to stabilize after the perturbation applied to both the conical and skyrmion lattice phase; significantly slower than the typical nanosecond timescale observed in micromagnetics. This prolonged relaxation is attributed to the formation and slow dissipation of local topological defects, such as emergent monopoles. By unveiling the experimental lifetime of these emergent singularities in a non-collinear magnetic system, our study highlights a universal relaxation mechanism in solitonic textures within the slow dynamics regime, offering new insights into topological physics and advanced information storage solutions., Comment: 6 pages, 4 figures

We consider Thompson Sampling (TS) for linear combinatorial semi-bandits and subgaussian rewards. We propose the first known TS whose finite-time regret does not scale exponentially with the dimension of the problem. We further show the "mismatched sampling paradox": A learner who knows the rewards distributions and samples from the correct posterior distribution can perform exponentially worse than a learner who does not know the rewards and simply samples from a well-chosen Gaussian posterior. The code used to generate the experiments is available at https://github.com/RaymZhang/CTS-Mismatched-Paradox, Comment: NeurIPS 2024

JWST observations of the young Galactic supernova remnant Cassiopeia A revealed an unexpected structure seen as a green emission feature in colored composite MIRI F1130W and F1280W images - hence dubbed the Green Monster - that stretches across the central parts of the remnant in projection. Combining the kinematic information from NIRSpec and MIRI MRS with the multi-wavelength imaging from NIRCam and MIRI, we associate the Green Monster with circumstellar material that was lost during an asymmetric mass-loss phase. MIRI images are dominated by dust emission but its spectra show emission lines from Ne, H and Fe with low radial velocities indicative of a CSM nature. An X-ray analysis of this feature in a companion paper (Vink et al. 2024) supports its CSM nature and detects significant blue shifting, thereby placing the Green Monster on the near side, in front of the Cas A SN remnant. The most striking features of the Green Monster are dozens of almost perfectly circular 1" - 3" sized holes, most likely created by interaction between high-velocity SN ejecta material and the CSM. Further investigation is needed to understand whether these holes were formed by small 8000-10500 km/s N-rich ejecta knots that penetrated and advanced out ahead of the remnant's 5000 - 6000 km/s outer blastwave, or by narrow ejecta fingers that protrude into the forward-shocked CSM. The detection of the Green Monster provides further evidence of the highly asymmetric mass-loss that Cas A's progenitor star underwent prior to explosion., Comment: 28 pages, 12 figures, resubmitted to ApJL after minor revision, comments welcome

The UNet architecture has transformed image segmentation. UNet's versatility and accuracy have driven its widespread adoption, significantly advancing fields reliant on machine learning problems with images. In this work, we give a clear and concise mathematical explanation of UNet. We explain what is the meaning and function of each of the components of UNet. We will show that UNet is solving a control problem. We decompose the control variables using multigrid methods. Then, operator-splitting techniques is used to solve the problem, whose architecture exactly recovers the UNet architecture. Our result shows that UNet is a one-step operator-splitting algorithm for the control problem.

Language models (LMs) can exhibit human-like behaviour, but it is unclear how to describe this behaviour without undue anthropomorphism. We formalise a behaviourist view of LM character traits: qualities such as truthfulness, sycophancy, or coherent beliefs and intentions, which may manifest as consistent patterns of behaviour. Our theory is grounded in empirical demonstrations of LMs exhibiting different character traits, such as accurate and logically coherent beliefs, and helpful and harmless intentions. We find that the consistency with which LMs exhibit certain character traits varies with model size, fine-tuning, and prompting. In addition to characterising LM character traits, we evaluate how these traits develop over the course of an interaction. We find that traits such as truthfulness and harmfulness can be stationary, i.e., consistent over an interaction, in certain contexts, but may be reflective in different contexts, meaning they mirror the LM's behavior in the preceding interaction. Our formalism enables us to describe LM behaviour precisely in intuitive language, without undue anthropomorphism., Comment: accepted as Findings of EMNLP2024

With sizable volatile envelopes but smaller radii than the solar system ice giants, sub-Neptunes have been revealed as one of the most common types of planet in the galaxy. While the spectroscopic characterization of larger sub-Neptunes (2.5-4R$_\oplus$) has revealed hydrogen-dominated atmospheres, smaller sub-Neptunes (1.6--2.5R$_\oplus$) could either host thin, rapidly evaporating hydrogen-rich atmospheres or be stable metal-rich "water worlds" with high mean molecular weight atmospheres and a fundamentally different formation and evolutionary history. Here, we present the 0.6--2.8$\mu$m JWST NIRISS/SOSS transmission spectrum of GJ 9827 d, the smallest (1.98 R$_\oplus$) warm (T$_\mathrm{eq, A_B=0.3} \sim 620$K) sub-Neptune where atmospheric absorbers have been detected to date. Our two transit observations with NIRISS/SOSS, combined with the existing HST/WFC3 spectrum, enable us to break the clouds-metallicity degeneracy. We detect water in a highly metal-enriched "steam world" atmosphere (O/H of $\sim 4$ by mass and H$_2$O found to be the background gas with a volume mixing ratio of >31%). We further show that these results are robust to stellar contamination through the transit light source effect. We do not detect escaping metastable He, which, combined with previous nondetections of escaping He and H, supports the steam atmosphere scenario. In water-rich atmospheres, hydrogen loss driven by water photolysis happens predominantly in the ionized form which eludes observational constraints. We also detect several flares in the NIRISS/SOSS light-curves with far-UV energies of the order of 10$^{30}$ erg, highlighting the active nature of the star. Further atmospheric characterization of GJ 9827 d probing carbon or sulfur species could reveal the origin of its high metal enrichment., Comment: 37 pages, 18 figures, Accepted for publication in ApJL

Structured internal representations (cognitive maps) shape cognition, from imagining the future and counterfactual past, to transferring knowledge to new settings. Our understanding of how such representations are formed and maintained in biological and artificial neural networks has grown enormously. The cognitive mapping hypothesis of schizophrenia extends this enquiry to psychiatry, proposing that diverse symptoms - from delusions to conceptual disorganisation - stem from abnormalities in how the brain forms structured representations. These abnormalities may arise from a confluence of neurophysiological perturbations (excitation-inhibition imbalance, resulting in attractor instability and impaired representational capacity), and/or environmental factors such as early life psychosocial stressors (which impinge on representation learning). This proposal thus links knowledge of neural circuit abnormalities, environmental risk factors, and symptoms., Comment: 25 pages, 2 figures, 1 table, 3 boxes. Final accepted version for publication at "Trends in Cognitive Science" 2024

Diffusion models have revolutionized image generation, and their extension to video generation has shown promise. However, current video diffusion models~(VDMs) rely on a scalar timestep variable applied at the clip level, which limits their ability to model complex temporal dependencies needed for various tasks like image-to-video generation. To address this limitation, we propose a frame-aware video diffusion model~(FVDM), which introduces a novel vectorized timestep variable~(VTV). Unlike conventional VDMs, our approach allows each frame to follow an independent noise schedule, enhancing the model's capacity to capture fine-grained temporal dependencies. FVDM's flexibility is demonstrated across multiple tasks, including standard video generation, image-to-video generation, video interpolation, and long video synthesis. Through a diverse set of VTV configurations, we achieve superior quality in generated videos, overcoming challenges such as catastrophic forgetting during fine-tuning and limited generalizability in zero-shot methods.Our empirical evaluations show that FVDM outperforms state-of-the-art methods in video generation quality, while also excelling in extended tasks. By addressing fundamental shortcomings in existing VDMs, FVDM sets a new paradigm in video synthesis, offering a robust framework with significant implications for generative modeling and multimedia applications., Comment: Code at https://github.com/Yaofang-Liu/FVDM