Search

Detections of fast X-ray transients (FXTs) have been accrued over the last few decades. However, their origin has remained mysterious. There is now rapid progress thanks to timely discoveries and localisations with the Einstein Probe mission. Early results indicate that FXTs may frequently, but not always, be associated with gamma-ray bursts (GRBs). Here, we report on the multi-wavelength counterpart of FXT EP240414a, which has no reported gamma-ray counterpart. The transient is located 25.7~kpc in projection from a massive galaxy at $z=0.40$. We perform comprehensive photometric and spectroscopic follow-up. The optical light curve shows at least three distinct emission episodes with timescales of $\sim 1, 4$ and 15 days and peak absolute magnitudes of $M_R \sim -20$, $-21$, and $-19.5$, respectively. The optical spectrum at early times is extremely blue, inconsistent with afterglow emission. It may arise from the interaction of both jet and supernova shock waves with the stellar envelope and a dense circumstellar medium, as has been suggested for some Fast Blue Optical Transients (LFBOTs). At late times, the spectrum evolves to a broad-lined~Type~Ic supernova, similar to those seen in collapsar long-GRBs. This implies that the progenitor of EP240414a is a massive star creating a jet-forming supernova inside a dense envelope, resulting in an X-ray outburst with a luminosity of $\sim 10^{48}$ erg s$^{-1}$, and the complex observed optical/IR light curves. If correct, this argues for a causal link between the progenitors of long-GRBs, FXTs and LFBOTs., Comment: 36 pages, 13 figures, submitted to ApJ

Images captured in challenging environments--such as nighttime, foggy, rainy weather, and underwater--often suffer from significant degradation, resulting in a substantial loss of visual quality. Effective restoration of these degraded images is critical for the subsequent vision tasks. While many existing approaches have successfully incorporated specific priors for individual tasks, these tailored solutions limit their applicability to other degradations. In this work, we propose a universal network architecture, dubbed "ReviveDiff", which can address a wide range of degradations and bring images back to life by enhancing and restoring their quality. Our approach is inspired by the observation that, unlike degradation caused by movement or electronic issues, quality degradation under adverse conditions primarily stems from natural media (such as fog, water, and low luminance), which generally preserves the original structures of objects. To restore the quality of such images, we leveraged the latest advancements in diffusion models and developed ReviveDiff to restore image quality from both macro and micro levels across some key factors determining image quality, such as sharpness, distortion, noise level, dynamic range, and color accuracy. We rigorously evaluated ReviveDiff on seven benchmark datasets covering five types of degrading conditions: Rainy, Underwater, Low-light, Smoke, and Nighttime Hazy. Our experimental results demonstrate that ReviveDiff outperforms the state-of-the-art methods both quantitatively and visually.

This paper introduces a novel track-length extension fitting algorithm for measuring the kinetic energies of inelastically interacting particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe the impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.

Antiferromagnets that display very small stray magnetic field are ideal for spintronic applications. Of particular interest are non-collinear, chiral antiferromagnets of the type Mn3X (X=Sn, Ge), which display a large magnetotransport response that is correlated with their antiferromagnetic ordering. The ability to read out and manipulate this ordering is crucial for their integration into spintronic devices. These materials exhibit a tiny unbalanced magnetic moment such that a large external magnetic field can, in principle, be used to set the material into a single antiferromagnetic domain. However, in thin films of Mn3Sn, we find that such fields induce only a partial magnetic ordering. By detecting two orthogonal in-plane components of the magnetic order vector, we find that the non-switchable fraction has a unidirectional anisotropy. This also enables us to visualize switching along multiple easy axes in Mn3Sn. Studying the switching at the nanoscale allows us to correlate the pining behavior to crystal grain boundaries in the Mn3Sn nanowire structures.

Quantum optimization, both for classical and quantum functions, is one of the most well-studied applications of quantum computing, but recent trends have relied on hybrid methods that push much of the fine-tuning off onto costly classical algorithms. Feedback-based quantum algorithms, such as FALQON, avoid these fine-tuning problems but at the cost of additional circuit depth and a lack of convergence guarantees. In this work, we take the local greedy information collected by Lyapunov feedback control and develop an analytic framework to use it to perturbatively update previous control layers, similar to the global optimal control achievable using Pontryagin optimal control. This perturbative methodology, which we call Feedback Optimally Controlled Quantum States (FOCQS), can be used to improve the results of feedback-based algorithms, like FALQON. Furthermore, this perturbative method can be used to push smooth annealing-like control protocol closer to the control optimum, even providing and iterative approach, albeit with diminishing returns. In numerical testing, we show improvements in convergence and required depth due to these methods over existing quantum feedback control methods., Comment: 13 pages, 3 figures

We conducted controlled experimental bias audits for four versions of ChatGPT, which we asked to recommend an opening offer in salary negotiations for a new hire. We submitted 98,800 prompts to each version, systematically varying the employee's gender, university, and major, and tested prompts in voice of each side of the negotiation: the employee versus employer. We find ChatGPT as a multi-model platform is not robust and consistent enough to be trusted for such a task. We observed statistically significant salary offers when varying gender for all four models, although with smaller gaps than for other attributes tested. The largest gaps were different model versions and between the employee- vs employer-voiced prompts. We also observed substantial gaps when varying university and major, but many of the biases were not consistent across model versions. We tested for fictional and fraudulent universities and found wildly inconsistent results across cases and model versions. We make broader contributions to the AI/ML fairness literature. Our scenario and our experimental design differ from mainstream AI/ML auditing efforts in key ways. Bias audits typically test discrimination for protected classes like gender, which we contrast with testing non-protected classes of university and major. Asking for negotiation advice includes how aggressive one ought to be in a negotiation relative to known empirical salary distributions and scales, which is a deeply contextual and personalized task that has no objective ground truth to validate. These results raise concerns for the specific model versions we tested and ChatGPT as a multi-model platform in continuous development. Our epistemology does not permit us to definitively certify these models as either generally biased or unbiased on the attributes we test, but our study raises matters of concern for stakeholders to further investigate.

We compute the nuclear dimension of extensions of C*-algebras involving commutative unital quotients and stable Kirchberg ideals. We identify the finite directed graphs whose C*-algebras are covered by this theorem., Comment: 30 pages

Quantum memories could benefit many devices in quantum information processing. For a quantum to be useful in real-world applications, the quantum memory must have a high recall efficiency. Here we demonstrate an efficient (up to 80%) spin-storage quantum memory in Er:YSO, using the 4-level rephased amplified spontaneous emission protocol. We show non-classical correlations between the ASE and RASE fields produced by the quantum memory. Also, we demonstrate the storage of 70 temporal modes, with a write time of 150 us, and a storage time of 25 us. Finally, a clear pathway is presented to improve the efficiency, storage time, and mode capacity. Such a device would have applications in quantum networking and measurement-based quantum computing., Comment: 9 pages, 10 figures

The next generation of smart imaging and vision systems will require compact and tunable optical computing hardware to perform high-speed and low-power image processing. These requirements are driving the development of computing metasurfaces to realize efficient front-end analog optical pre-processors, especially for edge-detection capability. Yet, there is still a lack of reconfigurable or programmable schemes, which may drastically enhance the impact of these devices at the system level. Here, we propose and experimentally demonstrate a reconfigurable flat optical image processor using low-loss phase-change nonlocal metasurfaces. The metasurface is configured to realize different transfer functions in spatial frequency space, when transitioning the phase-change material between its amorphous and crystalline phases. This enables edge detection and bright-field imaging modes on the same device. The metasurface is compatible with a large numerical aperture of ~0.5, making it suitable for high resolution coherent optical imaging microscopy. The concept of phase-change reconfigurable nonlocal metasurfaces may enable emerging applications of artificial intelligence-assisted imaging and vision devices with switchable multitasking., Comment: 20 pages, 5 figures

The filtering distribution captures the statistics of the state of a dynamical system from partial and noisy observations. Classical particle filters provably approximate this distribution in quite general settings; however they behave poorly for high dimensional problems, suffering weight collapse. This issue is circumvented by the ensemble Kalman filter which is an equal-weight interacting particle system. However, this finite particle system is only proven to approximate the true filter in the linear Gaussian case. In practice, however, it is applied in much broader settings; as a result, establishing its approximation properties more generally is important. There has been recent progress in the theoretical analysis of the algorithm, establishing stability and error estimates in non-Gaussian settings, but the assumptions on the dynamics and observation models rule out the unbounded vector fields that arise in practice and the analysis applies only to the mean field limit of the ensemble Kalman filter. The present work establishes error bounds between the filtering distribution and the finite particle ensemble Kalman filter when the dynamics and observation vector fields may be unbounded, allowing linear growth.

This paper presents SYBILGAT, a novel approach to Sybil detection in social networks using Graph Attention Networks (GATs). Traditional methods for Sybil detection primarily leverage structural properties of networks; however, they tend to struggle with a large number of attack edges and are often unable to simultaneously utilize both known Sybil and honest nodes. Our proposed method addresses these limitations by dynamically assigning attention weights to different nodes during aggregations, enhancing detection performance. We conducted extensive experiments in various scenarios, including pretraining in sampled subgraphs, synthetic networks, and networks under targeted attacks. The results show that SYBILGAT significantly outperforms the state-of-the-art algorithms, particularly in scenarios with high attack complexity and when the number of attack edges increases. Our approach shows robust performance across different network models and sizes, even as the detection task becomes more challenging. We successfully applied the model to a real-world Twitter graph with more than 269k nodes and 6.8M edges. The flexibility and generalizability of SYBILGAT make it a promising tool to defend against Sybil attacks in online social networks with only structural information., Comment: 9 pages, 1 figure, 6 tables

We investigate the detectability of Lyman-$\alpha$ (Ly$\alpha$) emission from galaxies at the onset of cosmic reionization, aiming to understand the conditions necessary for detecting high-redshift sources like JADES-GS-z13-1-LA at $z=13$. By integrating galaxy formation models with detailed intergalactic medium (IGM) reionization simulations, we construct high-redshift galaxy catalogs to model intrinsic Ly$\alpha$ profiles and assess their transmission through the IGM. For a galaxy with $M_{\rm UV}\sim -18.5$ like JADES-GS-z13-1-LA, our fiducial model predicts a Ly$\alpha$ transmission of ${\sim}13$% and there is a probability of observing Ly$\alpha$ emission with an equivalent width >40A of up to 10%. We also explore how variations in the UV ionizing escape fraction, dependent on host halo mass, impact Ly$\alpha$ detectability. Our findings reveal that reionization morphology significantly influences detection chances -- models where reionization is driven by low-mass galaxies can boost the detection probability to as much as 12%, while those driven by massive galaxies tend to reduce ionized regions around faint emitters, limiting their detectability. This study underscores the importance of reionization morphology in interpreting high-redshift Ly$\alpha$ observations., Comment: 7 pages, 3 figures, a theory follow up on 2408.16608, comments welcome

Due to their short orbital periods and relatively high flux ratios, irradiated brown dwarfs in binaries with white dwarfs offer better opportunities to study irradiated atmospheres than hot Jupiters, which have lower planet-to-star flux ratios. WD1032+011 is an eclipsing, tidally locked white dwarf-brown dwarf binary with a 9950 K white dwarf orbited by a 69.7 M$_{Jup}$ brown dwarf in a 0.09 day orbit. We present time-resolved Hubble Space Telescope Wide Field Camera 3 spectrophotometric data of WD1032+011. We isolate the phase-dependent spectra of WD1032+011B, finding a 210 K difference in brightness temperature between the dayside and nightside. The spectral type of the brown dwarf is identified as L1 peculiar, with atmospheric retrievals and comparison to field brown dwarfs showing evidence for a cloud-free atmosphere. The retrieved temperature of the dayside is $1748^{+66}_{-67}$ K, with a nightside temperature of $1555^{+76}_{-62}$ K, showing an irradiation-driven temperature contrast coupled with inefficient heat redistribution from the dayside to the nightside. The brown dwarf radius is inflated, likely due to the constant irradiation from the white dwarf, making it the only known inflated brown dwarf in an eclipsing white dwarf-brown dwarf binary., Comment: 20 pages, 23 figures. Accepted for Publication in MNRAS

Computational biomechanics models of the brain have become an important tool for investigating the brain responses to mechanical loads. The geometry, loading conditions, and constitutive properties of such brain models are well-studied and generally accepted. However, there is a lack of experimental evidence to support models of the layers of tissues (brain-skull interface) connecting the brain with the skull which determine boundary conditions for the brain. We present a new protocol for determining the biomechanical properties of the brain-skull interface and present the preliminary results (for a small number of tissue samples extracted from sheep cadaver heads). The method consists of biomechanical experiments using brain tissue and brain-skull complex (consisting of the brain tissue, brain-skull interface, and skull bone) and comprehensive computer simulation of the experiments using the finite element (FE) method. Application of the FE simulations allowed us to abandon the traditionally used approaches that rely on analytical formulations that assume cuboidal (or cylindrical) sample geometry when determining the parameters that describe the biomechanical behaviour of the brain tissue and brain-skull interface. In the simulations, we used accurate 3D geometry of the samples obtained from magnetic resonance images (MRIs). Our results indicate that the behaviour of the brain-skull interface under compressive loading appreciably differs from that under tension. Rupture of the interface was clearly visible for tensile load while no obvious indication of mechanical failure was observed under compression. These results suggest that assuming a rigid connection or frictionless sliding contact between the brain tissue and skull bone, the approaches often used in computational biomechanics models of the brain, may not accurately represent the mechanical behaviour of the brain-skull interface., Comment: Computational Biomechanics for Medicine (accepted). Springer

Intrinsic motivation (IM) and reward shaping are common methods for guiding the exploration of reinforcement learning (RL) agents by adding pseudo-rewards. Designing these rewards is challenging, however, and they can counter-intuitively harm performance. To address this, we characterize them as reward shaping in Bayes-Adaptive Markov Decision Processes (BAMDPs), which formalizes the value of exploration by formulating the RL process as updating a prior over possible MDPs through experience. RL algorithms can be viewed as BAMDP policies; instead of attempting to find optimal algorithms by solving BAMDPs directly, we use it at a theoretical framework for understanding how pseudo-rewards guide suboptimal algorithms. By decomposing BAMDP state value into the value of the information collected plus the prior value of the physical state, we show how psuedo-rewards can help by compensating for RL algorithms' misestimation of these two terms, yielding a new typology of IM and reward shaping approaches. We carefully extend the potential-based shaping theorem to BAMDPs to prove that when pseudo-rewards are BAMDP Potential-based shaping Functions (BAMPFs), they preserve optimal, or approximately optimal, behavior of RL algorithms; otherwise, they can corrupt even optimal learners. We finally give guidance on how to design or convert existing pseudo-rewards to BAMPFs by expressing assumptions about the environment as potential functions on BAMDP states.

Rydberg-mediated quantum optics is a useful route toward deterministic quantum information processing based on single photons and quantum networks, but is bottlenecked by the fast motional dephasing of Rydberg atoms. Here, we propose and experimentally demonstrate suppressing the motional dephasing by creating an {\it a priori} unknown but correct phase to each Rydberg atom in an atomic ensemble. The phase created is exactly proportional to the unknown velocity of the thermal motion, resulting in a condition as if no thermal motion occurs to the Rydberg atom upon the retrieval of the signal photon. Our experiments, though hampered by the noise of lasers and the environment, demonstrate more than one order of magnitude enhancement of the coherence time. The feasibility of realizing long-lived storage of single photons in strongly interacting Rydberg media sheds new light on Rydberg-mediated quantum nonlinear optics., Comment: 6 pages, 4 figures

Lacking terrestrial experimental data, our best constraints on the behavior of matter at high densities up to and above nuclear density arise from observations of neutron stars. Current constraints include those based on measurements of stellar masses, radii, and tidal deformabilities. Here we explore how orbits of primordial black holes - should they exist - inside neutron stars could provide complementary constraints on the nuclear equation of state (EOS). Specifically, we consider a sample of candidate EOSs, construct neutron star models for these EOSs, and compute orbits of primordial black holes inside these stars. We discuss how the pericenter advance of eccentric orbits, i.e. orbital precession, results in beat phenomena in the emitted gravitational wave signal. Observing this beat frequency could constrain the nuclear EOS and break possible degeneracies arising from other constraints, as well as provide information about the host star., Comment: 12 pages, 9 figures; PRD in press

Ensuring fairness in transaction fraud detection models is vital due to the potential harms and legal implications of biased decision-making. Despite extensive research on algorithmic fairness, there is a notable gap in the study of bias in fraud detection models, mainly due to the field's unique challenges. These challenges include the need for fairness metrics that account for fraud data's imbalanced nature and the tradeoff between fraud protection and service quality. To address this gap, we present a comprehensive fairness evaluation of transaction fraud models using public synthetic datasets, marking the first algorithmic bias audit in this domain. Our findings reveal three critical insights: (1) Certain fairness metrics expose significant bias only after normalization, highlighting the impact of class imbalance. (2) Bias is significant in both service quality-related parity metrics and fraud protection-related parity metrics. (3) The fairness through unawareness approach, which involved removing sensitive attributes such as gender, does not improve bias mitigation within these datasets, likely due to the presence of correlated proxies. We also discuss socio-technical fairness-related challenges in transaction fraud models. These insights underscore the need for a nuanced approach to fairness in fraud detection, balancing protection and service quality, and moving beyond simple bias mitigation strategies. Future work must focus on refining fairness metrics and developing methods tailored to the unique complexities of the transaction fraud domain.

Since Parker Solar Probe's (Parker's) first perihelion pass at the Sun, large amplitude Alfv\'en waves grouped in patches have been observed near the Sun throughout the mission. Several formation processes for these magnetic switchback patches have been suggested with no definitive consensus. To provide insight to their formation, we examine the heavy ion properties of several adjacent magnetic switchback patches around Parker's 11th perihelion pass capitalizing on a spacecraft lineup with Solar Orbiter where each samples the same solar wind streams over a large range of longitudes. Heavy ion properties (Fe/O, C$^{6+}$/C$^{5+}$, O$^{7+}$/O$^{6+}$) related to the wind's coronal origin, measured with Solar Orbiter can be linked to switchback patch structures identified near the Sun with Parker. We find that switchback patches do not contain distinctive ion and elemental compositional signatures different than the surrounding non-switchback solar wind. Both the patches and ambient wind exhibit a range of fast and slow wind qualities, indicating coronal sources with open and closed field lines in close proximity. These observations and modeling indicate switchback patches form in coronal hole boundary wind and with a range of source region magnetic and thermal properties. Furthermore, the heavy ion signatures suggest interchange reconnection and/or shear driven processes may play a role in their creation., Comment: Accepted for publication in ApJ on September 4th, 2024

We report evidence of galaxy assembly bias - the correlation between galaxy properties and biased secondary halo properties at fixed halo mass (M$_H$) - in the stellar-to-halo mass relation (SHMR) for red central galaxies from the Sloan Digital Sky Survey. In the M$_H = 10^{11.5}-10^{13.5} h^{-1}$ M$_{\odot}$ range, central galaxy stellar mass (M$_*$) is correlated with the number density of galaxies within $10 h^{-1}$ Mpc ($\delta_{10}$), a common proxy for halo formation time. This galaxy assembly bias signal is also present when M$_H$, M$_*$, and $\delta_{10}$ are substituted with group luminosity, galaxy luminosity, and metrics of the large-scale density field. To associate differences in $\delta_{10}$ with variations in halo formation time, we fitted a model that accounts for (1) errors in the M$_H$ measured by the Tinker 2021, 2022 group catalog and (2) the level of correlation between halo formation time and M$_*$ at fixed M$_H$. Fitting of this model yields that (1) errors in M$_H$ are 0.15 dex and (2) halo formation time and M$_*$ are strongly correlated (Spearman's rank correlation coefficient ~0.85). At fixed M$_H$, variations of ~0.4 dex in M$_*$ are associated with ~1-3 Gyr variations in halo formation time and in galaxy formation time (from stellar population fitting; Oyarz\'un et al. 2022). These results are indicative that halo properties other than M$_H$ can impact central galaxy assembly., Comment: Accepted for publication in ApJ. 11 pages and 6 figures

Random numbers are at the heart of every agent-based model (ABM) of health and disease. By representing each individual in a synthetic population, agent-based models enable detailed analysis of intervention impact and parameter sensitivity. Yet agent-based modeling has a fundamental signal-to-noise problem, in which small changes between simulations cannot be reliably differentiated from stochastic noise resulting from misaligned random number realizations. We introduce a novel methodology that eliminates noise due to misaligned random numbers, a first for agent-based modeling. Our approach enables meaningful individual-level analysis between ABM scenarios because all differences are driven by mechanistic effects rather than random number noise. We demonstrate the benefits of our approach on three disparate examples. Results consistently show reductions in the number of simulations required to achieve a given standard error with levels exceeding 10-fold for some applications., Comment: Updated title, improved abstract, and changed formatting

After leaving the Sun's corona, the solar wind continues to accelerate and cools, but more slowly than expected for a freely expanding adiabatic gas. We use in situ measurements from the Parker Solar Probe and Solar Orbiter spacecrafts to investigate a stream of solar wind as it traverses the inner heliosphere. The observations show heating and acceleration of the the plasma between the outer edge of the corona and near the orbit of Venus, in connection to the presence of large amplitude Alfv\'en waves. Alfv\'en waves are perturbations in the interplanetary magnetic field that transport energy. Our calculations show the damping and mechanical work performed by the Alfv\'en waves is sufficient to power the heating and acceleration of the fast solar wind in the inner heliosphere., Comment: This is the author's version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science on August 30 2024, DOI: 10.1126/science.adk6953

The study of animal diets and the proportional contribution that different foods make to their diets is an important task in ecology. Stable Isotope Mixing Models (SIMMs) are an important tool for studying an animal's diet and understanding how the animal interacts with its environment. We present cosimmr, a new R package designed to include covariates when estimating diet proportions in SIMMs, with simple functions to produce plots and summary statistics. The inclusion of covariates allows for users to perform a more in-depth analysis of their system and to gain new insights into the diets of the organisms being studied. A common problem with the previous generation of SIMMs is that they are very slow to produce a posterior distribution of dietary estimates, especially for more complex model structures, such as when covariates are included. The widely-used Markov chain Monte Carlo (MCMC) algorithm used by many traditional SIMMs often requires a very large number of iterations to reach convergence. In contrast, cosimmr uses Fixed Form Variational Bayes (FFVB), which we demonstrate gives up to an order of magnitude speed improvement with no discernible loss of accuracy. We provide a full mathematical description of the model, which includes corrections for trophic discrimination and concentration dependence, and evaluate its performance against the state of the art MixSIAR model. Whilst MCMC is guaranteed to converge to the posterior distribution in the long term, FFVB converges to an approximation of the posterior distribution, which may lead to sub-optimal performance. However we show that the package produces equivalent results in a fraction of the time for all the examples on which we test. The package is designed to be user-friendly and is based on the existing simmr framework.

We revisit Koiso's original examples of rigid infinitesimally deformable Einstein metrics. We show how to compute Koiso's obstruction to the integrability of the infinitesimal deformations on $\mathbb{CP}^{n}\times \mathbb{CP}^{1}$ using completely elementary complex differential geometry., Comment: 13 pages

Analyzing the cone photoreceptor pattern in images obtained from the living human retina using quantitative methods can be crucial for the early detection and management of various eye conditions. Confocal adaptive optics scanning light ophthalmoscope (AOSLO) imaging enables visualization of the cones from reflections of waveguiding cone photoreceptors. While there have been significant improvements in automated algorithms for segmenting cones in confocal AOSLO images, the process of labelling data remains labor-intensive and manual. This paper introduces a method based on deep learning (DL) for detecting and segmenting cones in AOSLO images. The models were trained on a semi-automatically labelled dataset of 20 AOSLO batches of images of 18 participants for 0$^{\circ}$, 1$^{\circ}$, and 2$^{\circ}$ from the foveal center. F1 scores were 0.968, 0.958, and 0.954 for 0$^{\circ}$, 1$^{\circ}$, and 2$^{\circ}$, respectively, which is better than previously reported DL approaches. Our method minimizes the need for labelled data by only necessitating a fraction of labelled cones, which is especially beneficial in the field of ophthalmology, where labelled data can often be limited.

Zero-field optically pumped magnetometers (OPMs) have emerged as an important technology for biomagnetism due to their ulta-sensitive performance, contained within a non-cryogenic small-scale sensor-head. The compactness of such OPMs is often achieved through simplified detection schemes, which typically provide only single-axis magnetic field information. However, multi-axis static magnetic fields on non-measurement axes cause a systematic error that manifests as amplitude and phase errors across the measurement axis. Here we present a triaxial operational technique for a compact zero-field OPM which suppresses multi-axis systematic errors through simultaneous measurement and closed-loop active control of the static magnetic fields across all axes. The demonstrated technique requires magnetic modulation across two axes while providing static field information for all three axes. We demonstrate this technique on a rubidium laboratory-based zero-field magnetometer, achieving a bandwidth of 380 Hz with sensitivities of $<25$ fT/$\sqrt{\rm{Hz}}$ across both transverse axes and $65$ fT/$\sqrt{\rm{Hz}}$ along the beam axis. Using the proposed triaxial technique, we demonstrate precise tracking of a 2 Hz triaxial vector test signal and suppression of systematic cross-axis projection errors over an extended period, $\simeq20$~min.

We present Sapiens, a family of models for four fundamental human-centric vision tasks -- 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction. Our models natively support 1K high-resolution inference and are extremely easy to adapt for individual tasks by simply fine-tuning models pretrained on over 300 million in-the-wild human images. We observe that, given the same computational budget, self-supervised pretraining on a curated dataset of human images significantly boosts the performance for a diverse set of human-centric tasks. The resulting models exhibit remarkable generalization to in-the-wild data, even when labeled data is scarce or entirely synthetic. Our simple model design also brings scalability -- model performance across tasks improves as we scale the number of parameters from 0.3 to 2 billion. Sapiens consistently surpasses existing baselines across various human-centric benchmarks. We achieve significant improvements over the prior state-of-the-art on Humans-5K (pose) by 7.6 mAP, Humans-2K (part-seg) by 17.1 mIoU, Hi4D (depth) by 22.4% relative RMSE, and THuman2 (normal) by 53.5% relative angular error. Project page: https://about.meta.com/realitylabs/codecavatars/sapiens., Comment: ECCV 2024 (Oral)

This work is part of ongoing efforts to detect Fast Radio Bursts (FRBs) using the Murchison Widefield Array (MWA) in a spectral window below 300 MHz. We used an image-based method based on the pilot study of Tingay et al. 2015, scaled up via massively parallel processing using a commercial supercomputer. We searched 87.6 hours of 2-second snapshot images, each covering 1165 square degrees of the EoR0 field, over a dispersion measure range of 170 to 1035 pc cm$^{-3}$. The large amount of data necessitated the construction of a series of filters to classify and reject the large number of false positives. Our search was more sensitive than any previous blind search using the MWA telescope, but we report no FRB detections, a result which is consistent with the extrapolation into the low-frequency domain of the results of Sokolowski et al. (2024). We obtain upper bounds on the event rate ranging from <1783 sky$^{-1}$day$^{-1}$ at a fluence of 392 Jy ms, to <31 sky$^{-1}$day$^{-1}$ at 8400 Jy ms, for our spectral window of 167-198 MHz. Our method was shown to be computationally efficient and scalable by the two or three orders of magnitude required to seriously test the model of Sokolowski et al. Our process is especially sensitive to detections of satellites and meteor trails and may find applications in the identification of these transients. We comment on future surveys using this method, with both the MWA and the SKA., Comment: 12 pages, 10 figures. To be published in Astronomical Journal

The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos.

This is a point-by-point response to a non-peer-reviewed document titled The Miscalibration of the Honeybee Odometer [arXiv:2405.12998], authored by L. Luebbert and L. Pachter, that was published on arXiv on 8 May 2024 and subsequently publicised via social and mainstream media. The authors never contacted Srinivasan or Tautz personally with their queries, nor did they seek clarification. They do not work in this research area, and they have drawn several unjustified conclusions that reflect a limited understanding of the data they have examined., Comment: Comment on arXiv:2405.12998 16 pages, 2 figures, 1 table

In the late stages of accretion leading up to the formation of planetesimals, particles grew to pebbles the size of 1-mm to tens of cm. That is the same size range that dominates the present-day comet mass loss. Meteoroids that size cause visible meteors on Earth. Here, we hypothesize that the size distribution and the physical and chemical properties of young meteoroid streams still contain information about the conditions in the solar nebula during these late stages of accretion. From observations of 47 young meteor showers, we find that freshly ejected meteoroids from long-period comets tend to have low bulk density and are distributed with equal surface area per log-mass interval (magnitude distribution index chi ~ 1.85), suggesting gentle accretion conditions. Jupiter-family comets, on the other hand, mostly produce meteoroids twice as dense and distributed with a steeper chi ~ 2.15 or even chi ~ 2.5, which implies that those pebbles grew from particles fragmenting in a collisional cascade or by catastrophic collisions, respectively. Both comet populations contain an admixture of compact materials that are sometimes sodium-poor, but Jupiter-family comets show a higher percentage (~8% on average) than long-period comet showers (~4%), and a wider range. While there are exceptions in both groups, the implication is that most long-period comets formed under gentle particle growth conditions, possibly near the 30 AU edge of the Trans Neptunian Disk, while most Jupiter family comets formed closer to the Sun where pebbles reached or passed the fragmentation barrier. This is possible if the Scattered Disk represents all objects scattered by Neptune during its migration, while the present-day outer Oort cloud formed only during and after the Sun had moved away from sibling stars., Comment: 82 pages, 14 figures, 3 tables

Optical trapping is having ever-increasing impact in science $-$ particularly biophysics, photonics and most recently in quantum optomechanics $-$ owing to its superior capability for manipulating nanoscale structures and materials. However, essentially all experimental optical trapping studies in the optical dipole regime have, to date, been dominated by the interaction between a material's electric polarizability, $\alpha_{e}$, and the electric part of the incident electromagnetic field, and therefore described by electric field intensity gradient forces. Optical trapping based on optical magnetic light-matter interactions has not been experimentally addressed despite it's immediate extension of the boundaries of optical trapping research and applications. This paper addresses this long-standing deficiency through the realization of optical magnetic trapping of large index of refraction (i.e., Si) nanoparticles and also presents a formalism for quantitative understanding of the experimental findings. Our experimental optical trapping results require including optical magnetic polarizability, $\alpha_{m}$, and electric-magnetic scattering forces associated with the Photonic Hall effect that are qualitatively and quantitatively validated by Maxwell stress tensor calculations. Our findings bring new opportunities for nanoparticle manipulation, potentially relax the limitations Ashkin claimed based on the optical Earnshaw's theorem, motivate optical matter formation by optical magnetic interactions, and suggest new N-body effects and symmetry breaking to drive dynamics of optical matter systems.

The observation of high-$T_c$ superconductivity (HTSC) in concomitant with pressure-induced orthorhombic-tetragonal structural transition in the bilayer La$_{3}$Ni$_2$O$_7$ has sparked hopes of achieving HTSC by stabilizing the tetragonal phase at ambient pressure. To mimic the effect of external physical pressures, the application of chemical pressure via replacing La$^3$$^+$ with smaller rare-earth R$^3$$^+$ has been considered as a potential route. Here we clarify the distinct effects of chemical and physical pressures on the structural transition of bilayer nickelates through a combined experimental and theoretical investigation. Contrary to general expectations, we find that substitutions of smaller R$^3$$^+$ for La$^3$$^+$ in La$_{3-x}$R$_x$Ni$_2$O$_{7-\delta}$, despite of an overall lattice contraction, produce stronger orthorhombic structural distortions and thus require higher pressures to induce the structural transition. We established a quantitative relationship between the critical pressure $P_c$ for structural transition and the average size of $A$-site cations. A linear extrapolation of $P_c$ versus <$r_A$> yields a putative critical value of <$r_A$>$_c$ ~ 1.23 angstrom for $P_c$ ~ 1 bar. The negative correlation between $P_c$ and <$r_A$> indicates that it is unlikely to reduce $P_c$ to ambient by replacing La$^3$$^+$ with smaller R$^3$$^+$ ions. Instead, partial substitution of La$^3$$^+$ with larger cations such as alkaline-earth Sr$^2$$^+$ or Ba$^2$$^+$ might be a feasible approach. Our results provide valuable guidelines in the quest of ambient-pressure HTSC in bilayer nickelates., Comment: 17 pages and 5 figures

Motivated by the study of state opioid policies, we propose a novel approach that uses autoregressive models for causal effect estimation in settings with panel data and staggered treatment adoption. Specifically, we seek to estimate of the impact of key opioid-related policies by quantifying the effects of must access prescription drug monitoring programs (PDMPs), naloxone access laws (NALs), and medical marijuana laws on opioid prescribing. Existing methods, such as differences-in-differences and synthetic controls, are challenging to apply in these types of dynamic policy landscapes where multiple policies are implemented over time and sample sizes are small. Autoregressive models are an alternative strategy that have been used to estimate policy effects in similar settings, but until this paper have lacked formal justification. We outline a set of assumptions that tie these models to causal effects, and we study biases of estimates based on this approach when key causal assumptions are violated. In a set of simulation studies that mirror the structure of our application, we also show that our proposed estimators frequently outperform existing estimators. In short, we justify the use of autoregressive models to provide robust evidence on the effectiveness of four state policies in combating the opioid crisis.

Speech rate has been shown to vary across social categories such as gender, age, and dialect, while also being conditioned by properties of speech planning. The effect of utterance length, where speech rate is faster and less variable for longer utterances, has also been shown to reduce the role of social factors once it has been accounted for, leaving unclear the relationship between social factors and speech production in conditioning speech rate. Through modelling of speech rate across 13 English speech corpora, it is found that utterance length has the largest effect on speech rate, though this effect itself varies little across corpora and speakers. While age and gender also modulate speech rate, their effects are much smaller in magnitude. These findings suggest utterance length effects may be conditioned by articulatory and perceptual constraints, and that social influences on speech rate should be interpreted in the broader context of how speech rate variation is structured., Comment: Proceedings of Interspeech 2024. 5 pages, 4 figures

In this commentary I review the claim by Luebbert and Pachter (arXiv:2405.12998v1) that the reported R-Squared value in Srinivasan et al. (Science, 287(5454):851-853, 2000), describing the relationship between distance to a food source and mean waggle duration of honeybee dances, was too high to be consistent with the reported means and standard deviations in the latter study. There is one serious limitation of the simulations conducted by Luebbert and Pachter, and two flaws that compromise their findings. The reported R-squared value of Srinivasan. et al. is within the expected range, as far as that can be determined given the limitations of the available data., Comment: 3 pages, one table, one figure; comment on arXiv:2405.12998v1

Fast radio bursts (FRBs) are transient radio signals of extragalactic origins that are subjected to propagation effects such as dispersion and scattering. It follows then that these signals hold information regarding the medium they have traversed and are hence useful as cosmological probes of the Universe. Recently, FRBs were used to make an independent measure of the Hubble Constant $H_0$, promising to resolve the Hubble tension given a sufficient number of detected FRBs. Such cosmological studies are dependent on FRB population statistics, cosmological parameters and detection biases, and thus it is important to accurately characterise each of these. In this work, we empirically characterise the sensitivity of the Fast Real-time Engine for Dedispersing Amplitudes (FREDDA) which is the current detection system for the Australian Square Kilometer Array Pathfinder (ASKAP). We coherently redisperse high-time resolution data of 13 ASKAP-detected FRBs and inject them into FREDDA to determine the recovered signal-to-noise ratios as a function of dispersion measure (DM). We find that for 11 of the 13 FRBs, these results are consistent with injecting idealised pulses. Approximating this sensitivity function with theoretical predictions results in a systematic error of 0.3$\,$km$\,$s$^{-1}\,$Mpc$^{-1}$ on $H_0$ when it is the only free parameter. Allowing additional parameters to vary could increase this systematic by up to $\sim1\,$km$\,$s$^{-1}\,$Mpc$^{-1}$. We estimate that this systematic will not be relevant until $\sim$400 localised FRBs have been detected, but will likely be significant in resolving the Hubble tension., Comment: 8 pages, 6 figures, Published in MNRAS

Hyperspectral imaging techniques have a unique ability to probe the inhomogeneity of material properties whether driven by compositional variation or other forms of phase segregation. In the doped cuprates, iridates, and related materials, scanning tunneling microscopy/spectroscopy (STM/STS) measurements have found the emergence of pseudogap 'puddles' from the macroscopically Mott insulating phase with increased doping. However, categorizing this hyperspectral data by electronic order is not trivial, and has often been done with ad hoc methods. In this paper we demonstrate the utility of $k$-means, a simple and easy-to-use unsupervised clustering method, as a tool for classifying heterogeneous scanning tunneling spectroscopy data by electronic order for Rh-doped Sr$_2$IrO$_{4}$, a cuprate-like material. Applied to STM data acquired within the Mott phase, $k$-means successfully identified areas of Mott order and of pseudogap order. The unsupervised nature of $k$-means limits avenues for bias, and provides clustered spectral shapes without a priori knowledge of the physics. Additionally, we demonstrate successful use of $k$-means as a preprocessing tool to constrain phenomenological function fitting. Clustering the data allows us to reduce the fitting parameter space, limiting over-fitting. We suggest $k$-means as a fast, simple model for processing hyperspectral data on materials of mixed electronic order., Comment: 6 pages, 4 figures

Supervised operator learning centers on the use of training data, in the form of input-output pairs, to estimate maps between infinite-dimensional spaces. It is emerging as a powerful tool to complement traditional scientific computing, which may often be framed in terms of operators mapping between spaces of functions. Building on the classical random features methodology for scalar regression, this paper introduces the function-valued random features method. This leads to a supervised operator learning architecture that is practical for nonlinear problems yet is structured enough to facilitate efficient training through the optimization of a convex, quadratic cost. Due to the quadratic structure, the trained model is equipped with convergence guarantees and error and complexity bounds, properties that are not readily available for most other operator learning architectures. At its core, the proposed approach builds a linear combination of random operators. This turns out to be a low-rank approximation of an operator-valued kernel ridge regression algorithm, and hence the method also has strong connections to Gaussian process regression. The paper designs function-valued random features that are tailored to the structure of two nonlinear operator learning benchmark problems arising from parametric partial differential equations. Numerical results demonstrate the scalability, discretization invariance, and transferability of the function-valued random features method., Comment: 36 pages, 1 table, 9 figures. SIGEST version of SIAM J. Sci. Comput. Vol. 43 No. 5 (2021) pp. A3212-A3243, hence text overlap with arXiv:2005.10224

Semantic segmentation involves assigning a specific category to each pixel in an image. While Vision Transformer-based models have made significant progress, current semantic segmentation methods often struggle with precise predictions in localized areas like object boundaries. To tackle this challenge, we introduce a new semantic segmentation architecture, ``MacFormer'', which features two key components. Firstly, using learnable agent tokens, a Mutual Agent Cross-Attention (MACA) mechanism effectively facilitates the bidirectional integration of features across encoder and decoder layers. This enables better preservation of low-level features, such as elementary edges, during decoding. Secondly, a Frequency Enhancement Module (FEM) in the decoder leverages high-frequency and low-frequency components to boost features in the frequency domain, benefiting object boundaries with minimal computational complexity increase. MacFormer is demonstrated to be compatible with various network architectures and outperforms existing methods in both accuracy and efficiency on benchmark datasets ADE20K and Cityscapes under different computational constraints., Comment: 13 pages, 7 figures, submitted to TIP

Fast radio burst (FRB) science primarily revolves around two facets: the origin of these bursts and their use in cosmological studies. This work follows from previous redshift-dispersion measure ($z$-DM) analyses in which we model instrumental biases and simultaneously fit population parameters and cosmological parameters to the observed population of FRBs. This sheds light on both the progenitors of FRBs and cosmological questions. Previously, we have completed similar analyses with data from the Australian Square Kilometer Array Pathfinder (ASKAP) and the Murriyang (Parkes) Multibeam system. With this manuscript, we additionally incorporate data from the Deep Synoptic Array (DSA) and the Five-hundred-meter Aperture Spherical Telescope (FAST), invoke a Markov chain Monte Carlo (MCMC) sampler and implement uncertainty in the Galactic DM contributions. The latter leads to larger uncertainties in derived model parameters than previous estimates despite the additional data. We provide refined constraints on FRB population parameters and derive a new constraint on the minimum FRB energy of log$\,E_{\mathrm{min}}$(erg)=39.49$^{+0.39}_{-1.48}$ which is significantly higher than bursts detected from strong repeaters. This result may indicate a low-energy turnover in the luminosity function or may suggest that strong repeaters have a different luminosity function to single bursts. We also predict that FAST will detect 25-41% of their FRBs at $z \gtrsim 2$ and DSA will detect 2-12% of their FRBs at $z \gtrsim 1$., Comment: 17 pages, 7 figures, submitted to PASA

Physical systems that display competitive non-linear dynamics have played a key role in the development of mathematical models of Nature. Important examples include predator-prey models in ecology, biology, consumer-resource models in economics, and reaction-diffusion equations in chemical reactions. However, as real world systems are embedded in complex environments, where it is difficult or even impossible to control external parameters, quantitative comparison between measurements and simple models remains challenging. This motivates the search for competitive dynamics in isolated physical systems, with precise control. An ideal candidate is laser excitation in dilute atomic ensembles. For example, atoms in highly-excited Rydberg states display rich many-body dynamics including ergodicity breaking, synchronisation and time crystals. Here, we demonstrate predator-prey dynamics by laser excitation and ionisation of Rydberg atoms in a room temperature vapour cell. Ionisation of excited atoms produce electric fields that suppress further excitation. This starves the ionisation process of resource, giving rise to predator-prey dynamics. By comparing our results to the Lotka-Volterra model, we demonstrate that as well applications in non-linear dynamics, our experiment has applications in metrology, and remote sensing of localised plasmas., Comment: 16 pages, 7 figures

Autoencoders have found widespread application in both their original deterministic form and in their variational formulation (VAEs). In scientific applications and in image processing it is often of interest to consider data that are viewed as functions; while discretisation (of differential equations arising in the sciences) or pixellation (of images) renders problems finite dimensional in practice, conceiving first of algorithms that operate on functions, and only then discretising or pixellating, leads to better algorithms that smoothly operate between resolutions. In this paper function-space versions of the autoencoder (FAE) and variational autoencoder (FVAE) are introduced, analysed, and deployed. Well-definedness of the objective governing VAEs is a subtle issue, particularly in function space, limiting applicability. For the FVAE objective to be well defined requires compatibility of the data distribution with the chosen generative model; this can be achieved, for example, when the data arise from a stochastic differential equation, but is generally restrictive. The FAE objective, on the other hand, is well defined in many situations where FVAE fails to be. Pairing the FVAE and FAE objectives with neural operator architectures that can be evaluated on any mesh enables new applications of autoencoders to inpainting, superresolution, and generative modelling of scientific data., Comment: 53 pages, 24 figures

ProtoDUNE Single-Phase (ProtoDUNE-SP) is a 770-ton liquid argon time projection chamber that operated in a hadron test beam at the CERN Neutrino Platform in 2018. We present a measurement of the total inelastic cross section of charged kaons on argon as a function of kaon energy using 6 and 7 GeV/$c$ beam momentum settings. The flux-weighted average of the extracted inelastic cross section at each beam momentum setting was measured to be 380$\pm$26 mbarns for the 6 GeV/$c$ setting and 379$\pm$35 mbarns for the 7 GeV/$c$ setting.

Quantum Fourier transformations are an essential component of many quantum algorithms, from prime factoring to quantum simulation. While the standard abelian QFT is well-studied, important variants corresponding to \emph{nonabelian} groups of interest have seen less development. In particular, fast nonabelian Fourier transformations are important components for both quantum simulations of field theories as well as approaches to the nonabelian hidden subgroup problem. In this work, we present fast quantum Fourier transformations for a number of nonabelian groups of interest for high energy physics, $\mathbb{BT}$, $\mathbb{BO}$, $\Delta(27)$, $\Delta(54)$, and $\Sigma(36\times3)$. For each group, we derive explicit quantum circuits and estimate resource scaling for fault-tolerant implementations. Our work shows that the development of a fast Fourier transformation can substantively reduce simulation costs by up to three orders of magnitude for the finite groups that we have investigated., Comment: 14 pages, 12 figures, 8 tables; minor typographical corrections

Hand-wearable robots, specifically exoskeletons, are designed to aid hands in daily activities, playing a crucial role in post-stroke rehabilitation and assisting the elderly. Our contribution to this field is a textile robotic glove with integrated actuators. These actuators, powered by pneumatic pressure, guide the user's hand to a desired position. Crafted from textile materials, our soft robotic glove prioritizes safety, lightweight construction, and user comfort. Utilizing the ruffles technique, integrated actuators guarantee high performance in blocking force and bending effectiveness. Here, we present a participant study confirming the effectiveness of our robotic device on a healthy participant group, exploiting EMG sensing., Comment: 3 pages, 4 figures, conference. arXiv admin note: text overlap with arXiv:2305.17720

The generation of $S_z=1$ triplet Cooper pairs has been predicted theoretically in superconducting-ferromagnetic hybrid heterostructures in the presence of spin-orbit coupling [F. S. Bergeret and I. V. Tokatly, Phys. Rev. B 89, 134517 (2014) and Jacobsen et al., Sci. Rep. 6, 23926 (2016)]. In this study, we experimentally investigate vertical Josephson junctions where the weak link is formed from a ferromagnetic layer with perpendicular magnetic anisotropy sandwiched by two non-magnetic layers with weak or strong spin-orbit coupling. We find that the decay of the Josephson coupling is reduced in the latter case, possibly indicating the presence of $S_z=1$ spin-triplet correlations. We speculate that the canted magnetization required for these correlations is provided by the interaction of magnetization with Meissner effect in the superconducting layers.

The detailed nature of dark energy remains a mystery, leaving the possibility that its effects might be explained by changes to the laws of gravity on large scales. The peculiar velocities of galaxies directly trace the strength of gravity on cosmic scales and provide a means to further constrain such models. We generate constraints on different scenarios of gravitational physics by measuring peculiar velocity and galaxy clustering two-point correlations, using redshifts and distances from the 6-degree Field Galaxy Survey and the Sloan Digital Sky Survey Peculiar Velocity samples, and fitting them against models characteristic of different cosmologies. Our best-fitting results are all found to be in statistical agreement with General Relativity, in which context we measure the low-redshift growth of structure to be $f\sigma_8 = 0.329^{+0.081}_{-0.083}$, consistent with the prediction of the standard $\Lambda$CDM model. We also fit the modified gravity scenarios of Dvali-Gabadadze-Porrati (nDGP) and a Hu-Sawicki model of $f(R)$ gravity, finding the $2\sigma$ limit of their characteristic parameters to be $r_cH_0/c>6.987$ and $-\log_{10}(|f_{R0}|)>4.703$, respectively. These constraints are comparable to other literature values, though it should be noted that they are significantly affected by the prior adopted for their characteristic parameters. When applied to much larger upcoming peculiar velocity surveys such as DESI, this method will place rapidly-improving constraints on modified gravity models of cosmic expansion and growth., Comment: 13 pages, 10 figures, 1 table, accepted for publication in MNRAS

We propose 6DGS to estimate the camera pose of a target RGB image given a 3D Gaussian Splatting (3DGS) model representing the scene. 6DGS avoids the iterative process typical of analysis-by-synthesis methods (e.g. iNeRF) that also require an initialization of the camera pose in order to converge. Instead, our method estimates a 6DoF pose by inverting the 3DGS rendering process. Starting from the object surface, we define a radiant Ellicell that uniformly generates rays departing from each ellipsoid that parameterize the 3DGS model. Each Ellicell ray is associated with the rendering parameters of each ellipsoid, which in turn is used to obtain the best bindings between the target image pixels and the cast rays. These pixel-ray bindings are then ranked to select the best scoring bundle of rays, which their intersection provides the camera center and, in turn, the camera rotation. The proposed solution obviates the necessity of an "a priori" pose for initialization, and it solves 6DoF pose estimation in closed form, without the need for iterations. Moreover, compared to the existing Novel View Synthesis (NVS) baselines for pose estimation, 6DGS can improve the overall average rotational accuracy by 12% and translation accuracy by 22% on real scenes, despite not requiring any initialization pose. At the same time, our method operates near real-time, reaching 15fps on consumer hardware., Comment: Project page: https://mbortolon97.github.io/6dgs/ Accepted to ECCV 2024

Without finger function, people with C5-7 spinal cord injury (SCI) regularly utilize wrist extension to passively close the fingers and thumb together for grasping. Wearable assistive grasping devices often focus on this familiar wrist-driven technique to provide additional support and amplify grasp force. Despite recent research advances in modernizing these tools, people with SCI often abandon such wearable assistive devices in the long term. We suspect that the wrist constraints imposed by such devices generate undesirable reach and grasp kinematics. Here we show that using continuous robotic motor assistance to give users more adaptability in their wrist posture prior to wrist-driven grasping reduces task difficulty and perceived exertion. Our results demonstrate that more free wrist mobility allows users to select comfortable and natural postures depending on task needs, which improves the versatility of the assistive grasping device for easier use across different hand poses in the arm's workspace. This behavior holds the potential to improve ease of use and desirability of future device designs through new modes of combining both body-power and robotic automation., Comment: 6 pages, 8 figures. Presented at the 2024 International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)