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The Purcell effect describes the enhancement of the spontaneous emission rate of an emitter near a resonant structure. However, evaluating the Purcell factor quantitatively and empirically is difficult due to the difficulties in measuring the electromagnetic nearfield of an optical resonance for calculation of the exact effective modal volume, especially with non-Hermitian resonators. Therefore, we propose a new analytical approach to circumvent the need to measure the nearfield and predict the Purcell enhancement with an analytical model and spectrally measurable parameters. Our proposed model predicts the averaged Purcell enhancement by metasurfaces on a photoluminescence medium, and is verified with experimental measurements and numerical simulations of nanoparticle arrays coupled to a fluorescent thin film. The model directly analyzes the photoluminescence enhancement and extraction efficiency of metasurface, and can be generalized to work with arbitrarily-shaped photoluminescent medium that is coupled to a resonator. This discovery will facilitate optimization of metasurfaces that support open cavity modes for efficient extraction of enhanced luminescence., Comment: 21 pages, 7 figures, 1 table

We introduce PLaMo-100B, a large-scale language model designed for Japanese proficiency. The model was trained from scratch using 2 trillion tokens, with architecture such as QK Normalization and Z-Loss to ensure training stability during the training process. Post-training techniques, including Supervised Fine-Tuning and Direct Preference Optimization, were applied to refine the model's performance. Benchmark evaluations suggest that PLaMo-100B performs well, particularly in Japanese-specific tasks, achieving results that are competitive with frontier models like GPT-4. The base model is available at https://huggingface.co/pfnet/plamo-100b.

This paper provides an overview of the techniques employed by Hibikino-Musashi@Home, which intends to participate in the domestic standard platform league. The team has developed a dataset generator for training a robot vision system and an open-source development environment running on a Human Support Robot simulator. The large language model powered task planner selects appropriate primitive skills to perform the task requested by users. The team aims to design a home service robot that can assist humans in their homes and continuously attends competitions to evaluate and improve the developed system.

SAFER-Splat (Simultaneous Action Filtering and Environment Reconstruction) is a real-time, scalable, and minimally invasive action filter, based on control barrier functions, for safe robotic navigation in a detailed map constructed at runtime using Gaussian Splatting (GSplat). We propose a novel Control Barrier Function (CBF) that not only induces safety with respect to all Gaussian primitives in the scene, but when synthesized into a controller, is capable of processing hundreds of thousands of Gaussians while maintaining a minimal memory footprint and operating at 15 Hz during online Splat training. Of the total compute time, a small fraction of it consumes GPU resources, enabling uninterrupted training. The safety layer is minimally invasive, correcting robot actions only when they are unsafe. To showcase the safety filter, we also introduce SplatBridge, an open-source software package built with ROS for real-time GSplat mapping for robots. We demonstrate the safety and robustness of our pipeline first in simulation, where our method is 20-50x faster, safer, and less conservative than competing methods based on neural radiance fields. Further, we demonstrate simultaneous GSplat mapping and safety filtering on a drone hardware platform using only on-board perception. We verify that under teleoperation a human pilot cannot invoke a collision. Our videos and codebase can be found at https://chengine.github.io/safer-splat.

We study topological defects in multi-axion models arising from multiple Peccei-Quinn (PQ) scalars. Using a simplified two-axion system, we reveal fundamental differences in the evolution of these defects compared to single-axion scenarios. This finding is particularly significant because, despite the fact that integrating out heavier axions reduces these models to an effective single PQ scalar theory at low energies, the actual physical behavior of topological defects differs markedly from single-axion predictions. Unlike single-axion models where conventional cosmic strings form, multi-axion scenarios with post-inflationary or mixed initial conditions generically produce networks of strings interconnected by high-tension domain walls. This results in a severe cosmological domain wall problem. We determine string-wall network instability conditions and discuss cosmological implications including the application to the QCD axion and gravitational wave generation. Our findings highlight that multi-axion dynamics can lead to qualitatively different outcomes for topological defects, challenging the conventional picture of cosmic evolution of topological defects based on single-axion models., Comment: 13 pages, 10 figures, 2 tables

There have been growing concerns around high-stake applications that rely on models trained with biased data, which consequently produce biased predictions, often harming the most vulnerable. In particular, biased medical data could cause health-related applications and recommender systems to create outputs that jeopardize patient care and widen disparities in health outcomes. A recent framework titled Fairness via AI posits that, instead of attempting to correct model biases, researchers must focus on their root causes by using AI to debias data. Inspired by this framework, we tackle bias detection in medical curricula using NLP models, including LLMs, and evaluate them on a gold standard dataset containing 4,105 excerpts annotated by medical experts for bias from a large corpus. We build on previous work by coauthors which augments the set of negative samples with non-annotated text containing social identifier terms. However, some of these terms, especially those related to race and ethnicity, can carry different meanings (e.g., "white matter of spinal cord"). To address this issue, we propose the use of Word Sense Disambiguation models to refine dataset quality by removing irrelevant sentences. We then evaluate fine-tuned variations of BERT models as well as GPT models with zero- and few-shot prompting. We found LLMs, considered SOTA on many NLP tasks, unsuitable for bias detection, while fine-tuned BERT models generally perform well across all evaluated metrics., Comment: Accepted for long presentation at the FAcctRec @ Recsys 2024

Electronic correlations lead to heavy quasiparticles in three-dimensional metals, and their collapse can destabilize magnetic moments. It is an open question whether there is an analogous instability in one-dimensional (1D) systems, unanswered due to the lack of metallic spin chains. We report neutron scattering measurements and Density Matrix Renormalization Group calculations establishing spinons in the correlated metal Ti4MnBi2, confirming it is 1D. Ti4MnBi2 is inherently frustrated, forming near a quantum critical point separating two T = 0 phases of the J1-J2 XXZ model. The lack of magnetic order above 0.3 K results from these quantum critical fluctuations, potentially compounded by Kondo moment compensation. Ti4MnBi2 provides the first experimental evidence that 1D magnetism, previously the exclusive domain of insulators, persists in metallic systems with moderate correlations., Comment: 13 pages, 4 figures

In magnetically ordered insulators, elementary quasiparticles manifest as spin waves - collective motions of localized magnetic moments propagating through the lattice - observed via inelastic neutron scattering. In effective spin-1/2 systems where geometric frustrations suppress static magnetic order, spin excitation continua can emerge, either from degenerate classical spin ground states or from entangled quantum spins characterized by emergent gauge fields and deconfined fractionalized excitations. Comparing the spin Hamiltonian with theoretical models can unveil the microscopic origins of these zero-field spin excitation continua. Here, we use neutron scattering to study spin excitations of the two-dimensional (2D) triangular-lattice effective spin-1/2 antiferromagnet CeMgAl11O19. Analyzing the spin waves in the field-polarized ferromagnetic state, we find that the spin Hamiltonian is close to an exactly solvable 2D triangular-lattice XXZ model, where degenerate 120$^\circ$ ordered ground states - umbrella states - develop in the zero temperature limit. We then find that the observed zero-field spin excitation continuum matches the calculated ensemble of spin waves from the umbrella state manifold, and thus conclude that CeMgAl11O19 is the first example of an exactly solvable spin liquid on a triangular lattice where the spin excitation continuum arises from the ground state degeneracy., Comment: 11 pages, 5 figures

As learning-to-rank models are increasingly deployed for decision-making in areas with profound life implications, the FairML community has been developing fair learning-to-rank (LTR) models. These models rely on the availability of sensitive demographic features such as race or sex. However, in practice, regulatory obstacles and privacy concerns protect this data from collection and use. As a result, practitioners may either need to promote fairness despite the absence of these features or turn to demographic inference tools to attempt to infer them. Given that these tools are fallible, this paper aims to further understand how errors in demographic inference impact the fairness performance of popular fair LTR strategies. In which cases would it be better to keep such demographic attributes hidden from models versus infer them? We examine a spectrum of fair LTR strategies ranging from fair LTR with and without demographic features hidden versus inferred to fairness-unaware LTR followed by fair re-ranking. We conduct a controlled empirical investigation modeling different levels of inference errors by systematically perturbing the inferred sensitive attribute. We also perform three case studies with real-world datasets and popular open-source inference methods. Our findings reveal that as inference noise grows, LTR-based methods that incorporate fairness considerations into the learning process may increase bias. In contrast, fair re-ranking strategies are more robust to inference errors. All source code, data, and experimental artifacts of our experimental study are available here: https://github.com/sewen007/hoiltr.git, Comment: This paper has been accepted by AAAI/AIES to the AIES 2024 conference

We revisit the minimal Nelson-Barr model for solving the strong CP problem through the idea of spontaneous CP breaking. The minimal model suffers from the quality problem, which means that the strong CP angle is generated by higher-dimensional operators and one-loop effects. Consequently, it has been considered that there is a cosmological domain wall problem and that leptogenesis does not work. We point out that just imposing an additional approximate global symmetry solves the quality problem. We also propose a simple solution to the domain wall problem and show that the thermal leptogenesis scenario works., Comment: 20 pages, 3 figures

We propose a new mechanism for isotropic cosmic birefringence with an axion-like field that rapidly oscillates during the recombination epoch. In conventional models, the field oscillation during the recombination epoch leads to a cancellation of the birefringence effect and significantly suppresses the EB spectrum of the cosmic microwave background (CMB) polarization. By introducing an asymmetric potential to the axion, this cancellation becomes incomplete, and a substantial EB spectrum can be produced. This mechanism also results in a washout of the EE spectrum, which can be probed in future CMB observations. Our findings suggest the possibility that an axion-like field responsible for isotropic cosmic birefringence can also account for a significant fraction of dark matter., Comment: 6 pages, 5 figures

We show that heavy axion domain walls induce domain walls of the QCD axion through a mixing between the heavy axion and the QCD axion, even when the pre-inflationary initial condition is assumed for the QCD axion. The induced domain walls arise because the effective $\theta$ parameter changes across the heavy axion domain walls, shifting the potential minimum of the QCD axion. When the heavy axion domain walls collapse, the induced QCD axion domain walls collapse as well. This novel mechanism for producing the QCD axions can explain dark matter even with the axion decay constant as small as ${\cal O}(10^{9})$ GeV. In particular, this scenario requires domain wall collapse near the QCD crossover, potentially accounting for the stochastic gravitational wave background suggested by recent pulsar timing array observations, including NANOGrav. Using this mechanism, it is also possible to easily create induced domain walls for string axions or axions with a large decay constant, which would otherwise be challenging. We also comment on the implications for cosmic birefringence using induced axion domain walls., Comment: 35 pages, 8 figures, v2: matches version published in JCAP

Visual sensors are not only becoming better at capturing high-quality images but also they have steadily increased their capabilities in processing data on their own on-chip. Yet the majority of VO pipelines rely on the transmission and processing of full images in a centralized unit (e.g. CPU or GPU), which often contain much redundant and low-quality information for the task. In this paper, we address the task of frame-to-frame rotational estimation but, instead of reasoning about relative motion between frames using the full images, distribute the estimation at pixel-level. In this paradigm, each pixel produces an estimate of the global motion by only relying on local information and local message-passing with neighbouring pixels. The resulting per-pixel estimates can then be communicated to downstream tasks, yielding higher-level, informative cues instead of the original raw pixel-readings. We evaluate the proposed approach on real public datasets, where we offer detailed insights about this novel technique and open-source our implementation for the future benefit of the community.

We point out that the rotation angle $\beta$ of cosmic birefringence, which is a recently reported parity-violating signal in the cosmic microwave background (CMB), has a phase ambiguity of $n\pi \,(n\in\mathbb{Z})$. This ambiguity has a significant impact on the interpretation of the origin of cosmic birefringence. Assuming an axion-like particle as the origin of cosmic birefringence, this ambiguity can be partly broken by the anisotropic cosmic birefringence and the shape of the CMB angular power spectra. We also discuss constraints on $\beta$ from existing experimental results., Comment: 6 pages, 6 figures

Biased information (recently termed bisinformation) continues to be taught in medical curricula, often long after having been debunked. In this paper, we introduce BRICC, a firstin-class initiative that seeks to mitigate medical bisinformation using machine learning to systematically identify and flag text with potential biases, for subsequent review in an expert-in-the-loop fashion, thus greatly accelerating an otherwise labor-intensive process. A gold-standard BRICC dataset was developed throughout several years, and contains over 12K pages of instructional materials. Medical experts meticulously annotated these documents for bias according to comprehensive coding guidelines, emphasizing gender, sex, age, geography, ethnicity, and race. Using this labeled dataset, we trained, validated, and tested medical bias classifiers. We test three classifier approaches: a binary type-specific classifier, a general bias classifier; an ensemble combining bias type-specific classifiers independently-trained; and a multitask learning (MTL) model tasked with predicting both general and type-specific biases. While MTL led to some improvement on race bias detection in terms of F1-score, it did not outperform binary classifiers trained specifically on each task. On general bias detection, the binary classifier achieves up to 0.923 of AUC, a 27.8% improvement over the baseline. This work lays the foundations for debiasing medical curricula by exploring a novel dataset and evaluating different training model strategies. Hence, it offers new pathways for more nuanced and effective mitigation of bisinformation., Comment: Under review

We study a primordial black hole (PBH) formation model based on the framework of the inhomogeneous Affleck-Dine (AD) mechanism, which can explain the seeds of supermassive black holes (SMBHs). This model, however, predicts strong clustering of SMBHs that is inconsistent with the observation of angular correlation of quasars. In this paper, we propose a modified model that can significantly reduce the PBH clustering on large scales by considering a time-dependent Hubble-induced mass during inflation. The quasar angular correlation is suppressed by the large Hubble-induced mass in the early stage of inflation while the small Hubble-induced mass in the late stage leads to the AD field fluctuations large enough for PBH formation as in the original model. As a result, the modified scenario can successfully explain the seeds of SMBHs., Comment: 26 pages, 7 figures

Let $\Omega$ be the {\em superspace ring} of polynomial-valued differential forms on affine $n$-space. The natural action of the symmetric group $\mathfrak{S}_n$ on $n$-space induces an action of $\mathfrak{S}_n$ on $\Omega$. The {\em superspace coinvariant ring} is the quotient $SR$ of $\Omega$ by the ideal generated by $\mathfrak{S}_n$-invariants with vanishing constant term. We give the first explicit basis of $SR$, proving a conjecture of Sagan and Swanson. Our techniques use the theory of hyperplane arrangements. We relate $SR$ to instances of the Solomon-Terao algebras of Abe-Maeno-Murai-Numata and use exact sequences relating the derivation modules of certain `southwest closed' arrangements to obtain the desired basis of $SR$., Comment: 26 pages

Focal-Plane Sensor-Processor Arrays (FPSP)s are an emerging technology that can execute vision algorithms directly on the image sensor. Unlike conventional cameras, FPSPs perform computation on the image plane -- at individual pixels -- enabling high frame rate image processing while consuming low power, making them ideal for mobile robotics. FPSPs, such as the SCAMP-5, use parallel processing and are based on the Single Instruction Multiple Data (SIMD) paradigm. In this paper, we present BIT-VIO, the first Visual Inertial Odometry (VIO) which utilises SCAMP-5.BIT-VIO is a loosely-coupled iterated Extended Kalman Filter (iEKF) which fuses together the visual odometry running fast at 300 FPS with predictions from 400 Hz IMU measurements to provide accurate and smooth trajectories., Comment: Accepted for Presentation Yokohama, Japan for IEEE 2024 ICRA

Surface lattice resonance supported on plasmonic nanoparticle arrays enhances light-matter interactions for applications such as photoluminescence enhancement. The photoluminescence process is enhanced through confining light beyond the diffraction limit and inducing stronger light-matter interaction. In this work, the absorption mechanisms of plasmonic nanoparticle arrays embedded with photoluminescent absorbers are analyzed. A modified coupled-mode theory that describes the optical behavior of the surface lattice resonance was developed and verified by numerical simulations. Based on the analytical model, different components of the absorption contributed by the nanoparticles and the absorbers as well as the resonant properties of each of the components are identified. The origin of difference in resonant behavior with different materials is also discovered by exploring the nearfield characteristics of surface lattice resonance composed with a variety of materials., Comment: 20 pages, 7 figures, 1 table

We present a thorough experimental investigation on single crystals of the rare-earth based frustrated quantum antiferromagnet Pr$_3$BWO$_9$, a purported spin-liquid candidate on the breathing kagome lattice. This material possesses a disordered ground state with an unusual excitation spectrum involving a coexistence of sharp spin-waves and broad continuum excitations. Nevertheless, we show through a combination of thermodynamic, magnetometric and spectroscopic probes with detailed theoretical modeling that it should be understood in a completely different framework. The crystal field splits the lowest quasi-doublet states into two singlets moderately coupled through frustrated superexchange, resulting in a simple effective Hamiltonian of an Ising model in a transverse magnetic field. While our neutron spectroscopy data do point to significant correlations within the kagome planes, the dominant interactions are out-of-plane, forming frustrated triangular spin-tubes through two competing ferro-antiferromagnetic bonds. The resulting ground state is a simple quantum paramagnet, but with significant modifications to both thermodynamic and dynamic properties due to small perturbations to the transverse field Ising model in the form of hyperfine enhanced nuclear moments and weak structural disorder., Comment: 21 pages, 12 figures

Sterile neutrino is a fascinating candidate for dark matter. In this paper, we examine the Affleck-Dine (AD) leptogenesis scenario generating a large lepton asymmetry, which can induce the resonant production of sterile neutrino dark matter via the Shi-Fuller (SF) mechanism. We also revisit the numerical calculation of the SF mechanism and the constraints from current X-ray and Lyman-$\alpha$ forest observations. We find that the AD leptogenesis scenario can explain the production of sterile neutrino dark matter by incorporating a non-topological soliton with a lepton charge called L-ball. Finally, we discuss an enhancement of second-order gravitational waves at the L-ball decay and investigate the testability of our scenario with future gravitational wave observations., Comment: 32 pages, 11 figures

We study the dynamics of axions at first-order phase transitions in non-Abelian gauge theories. When the duration of the phase transition is short compared to the timescale of the axion oscillations, the axion dynamics is similar to the trapped misalignment mechanism. On the other hand, if this is not the case, the axions are initially expelled from the inside of the bubbles, generating axion waves on the outside. Analogous to the Fermi acceleration, these axions gain energy by repeatedly scattering off the bubble walls. Once they acquire enough energy, they can enter the bubbles. The novel ``bubble misalignment mechanism'' can significantly enhance the axion abundance, compared to models where the axion mass is either constant or varies continuously as a function of temperature. The increase in axion abundance depends on the axion mass, the duration of the phase transition, and the bubble wall velocity. This mechanism results in a spatially inhomogeneous distribution of axions, which could lead to the formation of axion miniclusters. It has potential implications for the formation of oscillons/I-balls, axion warm dark matter, cosmic birefringence, and the production of dark photons., Comment: 15pages, 14figures, 1table, v2: added references, corrected an error in axion number at transmission, conclusions unchanged

Magnetic, thermodynamic, neutron diffraction and inelastic neutron scattering are used to study spin correlations in the easy-axis XXZ triangular lattice magnet K2Co(SeO3)2. Despite the presence of quasi-2D "supersolid" magnetic order, the low-energy excitation spectrum contains no sharp modes and is instead a broad and structured multi-particle continuum. Applying a weak magnetic field drives the system into an m = 1/3 fractional magnetization plateau phase and restores sharp spin wave modes. To some extent, the behavior at zero field can be understood in terms of spin wave decay. However, the presence of clear excitation minima at the M-points of the Brillouin zone suggest that the spinon language may provide a more adequate description, and signals a possible proximity to a Dirac spin liquid state.

We present a novel scalable, fully distributed, and online method for simultaneous localisation and extrinsic calibration for multi-robot setups. Individual a priori unknown robot poses are probabilistically inferred as robots sense each other while simultaneously calibrating their sensors and markers extrinsic using Gaussian Belief Propagation. In the presented experiments, we show how our method not only yields accurate robot localisation and auto-calibration but also is able to perform under challenging circumstances such as highly noisy measurements, significant communication failures or limited communication range., Comment: Published in IEEE Robotics and Automation Letters (RA-L) 2024

We present the PanAf20K dataset, the largest and most diverse open-access annotated video dataset of great apes in their natural environment. It comprises more than 7 million frames across ~20,000 camera trap videos of chimpanzees and gorillas collected at 14 field sites in tropical Africa as part of the Pan African Programme: The Cultured Chimpanzee. The footage is accompanied by a rich set of annotations and benchmarks making it suitable for training and testing a variety of challenging and ecologically important computer vision tasks including ape detection and behaviour recognition. Furthering AI analysis of camera trap information is critical given the International Union for Conservation of Nature now lists all species in the great ape family as either Endangered or Critically Endangered. We hope the dataset can form a solid basis for engagement of the AI community to improve performance, efficiency, and result interpretation in order to support assessments of great ape presence, abundance, distribution, and behaviour and thereby aid conservation efforts., Comment: Accepted at IJCV

We present photometric, spectroscopic and polarimetric observations of the intermediate-luminosity Type IIP supernova (SN) 2021gmj from 1 to 386 days after the explosion. The peak absolute V-band magnitude of SN 2021gmj is -15.5 mag, which is fainter than that of normal Type IIP SNe. The spectral evolution of SN 2021gmj resembles that of other sub-luminous supernovae: the optical spectra show narrow P-Cygni profiles, indicating a low expansion velocity. We estimate the progenitor mass to be about 12 Msun from the nebular spectrum and the 56Ni mass to be about 0.02 Msun from the bolometric light curve. We also derive the explosion energy to be about 3 x 10^{50} erg by comparing numerical light curve models with the observed light curves. Polarization in the plateau phase is not very large, suggesting nearly spherical outer envelope. The early photometric observations capture the rapid rise of the light curve, which is likely due to the interaction with a circumstellar material (CSM). The broad emission feature formed by highly-ionized lines on top of a blue continuum in the earliest spectrum gives further indication of the CSM at the vicinity of the progenitor. Our work suggests that a relatively low-mass progenitor of an intermediate-luminosity Type IIP SN can also experience an enhanced mass loss just before the explosion, as suggested for normal Type IIP SNe., Comment: 18 pages, 16 figures, resubmitted to MNRAS after addressing referee comments

Many high-stakes decision-making problems, such as those found within cybersecurity and economics, can be modeled as competitive resource allocation games. In these games, multiple players must allocate limited resources to overcome their opponent(s), while minimizing any induced individual losses. However, existing means of assessing the performance of resource allocation algorithms are highly disparate and problem-dependent. As a result, evaluating such algorithms is unreliable or impossible in many contexts and applications, especially when considering differing levels of feedback. To resolve this problem, we propose a generalized definition of payoff which uses an arbitrary user-provided function. This unifies performance evaluation under all contexts and levels of feedback. Using this definition, we develop metrics for evaluating player performance, and estimators to approximate them under uncertainty (i.e., bandit or semi-bandit feedback). These metrics and their respective estimators provide a problem-agnostic means to contextualize and evaluate algorithm performance. To validate the accuracy of our estimator, we explore the Colonel Blotto ($\mathcal{CB}$) game as an example. To this end, we propose a graph-pruning approach to efficiently identify feasible opponent decisions, which are used in computing our estimation metrics. Using various resource allocation algorithms and game parameters, a suite of $\mathcal{CB}$ games are simulated and used to compute and evaluate the quality of our estimates. These simulations empirically show our approach to be highly accurate at estimating the metrics associated with the unseen outcomes of an opponent's latent behavior.

Fractional magnetisation plateaus, in which the magnetisation is pinned at a fraction of its saturated value within a range of external magnetic field, are spectacular macroscopic manifestations of the collective quantum behaviours. One prominent example of the plateau phase is found in spin-1/2 triangular-lattice antiferromagnets featuring strong geometrical frustration, and is often interpreted as quantum-fluctuation-stabilised state in magnetic field via the "order-by-disorder" mechanism. Here, we observe an unprecedented 1/3 magnetisation plateau between 5.2 and 7.4 T at 2 K in a spin-1 antiferromagnet Na$_3$Ni$_2$BiO$_6$ with a honeycomb lattice, where conventionally no geometrical frustration is anticipated. By carrying out elastic neutron scattering measurements, we propose the spin structure of the plateau phase to be an unusual partial spin-flop ferrimagnetic order, transitioning from the zigzag antiferromagnetic order in zero field. Our theoretical calculations show that the plateau phase is stabilised by the bond-anisotropic Kitaev interaction. These results provide a new paradigm for the exploration of rich quantum phases in frustrated magnets and exotic Kitaev physics in high-spin systems., Comment: Submitted version, 10 pages, 5 figures. Final version has been published in Nature Physics

Social media may disseminate medical claims that highlight misleading correlations between social identifiers and diseases due to not accounting for structural determinants of health. Our research aims to identify biased medical claims on Twitter and measure their spread. We propose a machine learning framework that uses two models in tandem: RoBERTa to detect medical claims and DistilBERT to classify bias. After identifying original biased medical claims, we conducted a retweet cascade analysis, computing their individual reach and rate of spread. Tweets containing biased claims were found to circulate faster and further than unbiased claims., Comment: Accepted at 2023 MIT Undergraduate Research Technology Conference (URTC'23)

In this note, we study Betti splittings of cover ideals of bipartite graphs. We prove that if $J \subset \Bbbk [x_1,\dots,x_n]$ is the cover ideal of a bipartite graph then the $x_i$-partition of $J$ is a Betti splitting for any $i$. We also prove that multigraded Betti numbers of any squarefree monomial ideal can appear in a certain part of multigraded Betti numbers of the cover ideal of a bipartite graph.

We present a new mechanism to generate a coherently oscillating dark vector field from axion-SU(2) gauge field dynamics during inflation. The SU(2) gauge field acquires a nonzero background sourced by an axion during inflation, and it acquires a mass through spontaneous symmetry breaking after inflation. We find that the coherent oscillation of the dark vector field can account for dark matter in the mass range of $10^{-13}-1$ eV in a minimal setup. In a more involved scenario, the range can be wider down to the fuzzy dark matter region. One of the dark vector fields can be identified as the dark photon, in which case this mechanism evades the notorious constraints for isocurvature perturbation, statistical anisotropy, and the absence of ghosts that exist in the usual misalignment production scenarios. Phenomenological implications are discussed., Comment: 30 pages, 5 figures. Accepted for publication in JCAP

We present the first application of 3D Gaussian Splatting in monocular SLAM, the most fundamental but the hardest setup for Visual SLAM. Our method, which runs live at 3fps, utilises Gaussians as the only 3D representation, unifying the required representation for accurate, efficient tracking, mapping, and high-quality rendering. Designed for challenging monocular settings, our approach is seamlessly extendable to RGB-D SLAM when an external depth sensor is available. Several innovations are required to continuously reconstruct 3D scenes with high fidelity from a live camera. First, to move beyond the original 3DGS algorithm, which requires accurate poses from an offline Structure from Motion (SfM) system, we formulate camera tracking for 3DGS using direct optimisation against the 3D Gaussians, and show that this enables fast and robust tracking with a wide basin of convergence. Second, by utilising the explicit nature of the Gaussians, we introduce geometric verification and regularisation to handle the ambiguities occurring in incremental 3D dense reconstruction. Finally, we introduce a full SLAM system which not only achieves state-of-the-art results in novel view synthesis and trajectory estimation but also reconstruction of tiny and even transparent objects., Comment: CVPR2024 Highlight. First two authors contributed equally to this work. Project Page: https://rmurai.co.uk/projects/GaussianSplattingSLAM/