Search

Autonomous robotic search has important applications in robotics, such as the search for signs of life after a disaster. When \emph{a priori} information is available, for example in the form of a distribution, a planner can use that distribution to guide the search. Ergodic search is one method that uses the information distribution to generate a trajectory that minimizes the ergodic metric, in that it encourages the robot to spend more time in regions with high information and proportionally less time in the remaining regions. Unfortunately, prior works in ergodic search do not perform well in complex environments with obstacles such as a building's interior or a maze. To address this, our work presents a modified ergodic metric using the Laplace-Beltrami eigenfunctions to capture map geometry and obstacle locations within the ergodic metric. Further, we introduce an approach to generate trajectories that minimize the ergodic metric while guaranteeing obstacle avoidance using measure-preserving vector fields. Finally, we leverage the divergence-free nature of these vector fields to generate collision-free trajectories for multiple agents. We demonstrate our approach via simulations with single and multi-agent systems on maps representing interior hallways and long corridors with non-uniform information distribution. In particular, we illustrate the generation of feasible trajectories in complex environments where prior methods fail., Comment: 15 pages, accepted to DARS 2024

Studying and building datasets for dialogue tasks is both expensive and time-consuming due to the need to recruit, train, and collect data from study participants. In response, much recent work has sought to use large language models (LLMs) to simulate both human-human and human-LLM interactions, as they have been shown to generate convincingly human-like text in many settings. However, to what extent do LLM-based simulations \textit{actually} reflect human dialogues? In this work, we answer this question by generating a large-scale dataset of 100,000 paired LLM-LLM and human-LLM dialogues from the WildChat dataset and quantifying how well the LLM simulations align with their human counterparts. Overall, we find relatively low alignment between simulations and human interactions, demonstrating a systematic divergence along the multiple textual properties, including style and content. Further, in comparisons of English, Chinese, and Russian dialogues, we find that models perform similarly. Our results suggest that LLMs generally perform better when the human themself writes in a way that is more similar to the LLM's own style.

The Schwarzschild radii of primordial black holes (PBHs) in the mass range of 6x10^{14}g to 4x10^{19}g match the sizes of nuclei to atoms. I discuss the resulting quantum-mechanical suppression in the accretion of matter by PBHs in dense astrophysical environments, such as planets or stars., Comment: Accepted for publication in The Astrophysical Journal Letters, 7 pages

In the Quantum-Train (QT) framework, mapping quantum state measurements to classical neural network weights is a critical challenge that affects the scalability and efficiency of hybrid quantum-classical models. The traditional QT framework employs a multi-layer perceptron (MLP) for this task, but it struggles with scalability and interpretability. To address these issues, we propose replacing the MLP with a tensor network-based model and introducing a distributed circuit ansatz designed for large-scale quantum machine learning with multiple small quantum processing unit nodes. This approach enhances scalability, efficiently represents high-dimensional data, and maintains a compact model structure. Our enhanced QT framework retains the benefits of reduced parameter count and independence from quantum resources during inference. Experimental results on benchmark datasets demonstrate that the tensor network-based QT framework achieves competitive performance with improved efficiency and generalization, offering a practical solution for scalable hybrid quantum-classical machine learning., Comment: 4 pages, 3 figures

We present \textsc{Mathpop}, a novel method to infer the globular cluster (GC) counts in ultra-diffuse galaxies (UDGs) and low-surface brightness galaxies (LSBGs). Many known UDGs have a surprisingly high ratio of GC number to surface brightness. However, standard methods to infer GC counts in UDGs face various challenges, such as photometric measurement uncertainties, GC membership uncertainties, and assumptions about the GC luminosity functions (GCLFs). \textsc{Mathpop} tackles these challenges using the mark-dependent thinned point process, enabling joint inference of the spatial and magnitude distributions of GCs. In doing so, \textsc{Mathpop} allows us to infer and quantify the uncertainties in both GC counts and GCLFs with minimal assumptions. As a precursor to \textsc{Mathpop}, we also address the data uncertainties coming from the selection process of GC candidates: we obtain probabilistic GC candidates instead of the traditional binary classification based on the color--magnitude diagram. We apply \textsc{Mathpop} to 40 LSBGs in the Perseus cluster using GC catalogs from a \textit{Hubble Space Telescope} imaging program. We then compare our results to those from an independent study using the standard method. We further calibrate and validate our approach through extensive simulations. Our approach reveals two LSBGs having GCLF turnover points much brighter than the canonical value with Bayes' factor being $\sim4.5$ and $\sim2.5$, respectively. An additional crude maximum-likelihood estimation shows that their GCLF TO points are approximately $0.9$~mag and $1.1$~mag brighter than the canonical value, with $p$-value $\sim 10^{-8}$ and $\sim 10^{-5}$, respectively., Comment: 8 figures, 5 tables; submitted to ApJ, comments are welcomed

Expected points is a value function fundamental to player evaluation and strategic in-game decision-making across sports analytics, particularly in American football. To estimate expected points, football analysts use machine learning tools, which are not equipped to handle certain challenges. They suffer from selection bias, display counter-intuitive artifacts of overfitting, do not quantify uncertainty in point estimates, and do not account for the strong dependence structure of observational football data. These issues are not unique to American football or even sports analytics; they are general problems analysts encounter across various statistical applications, particularly when using machine learning in lieu of traditional statistical models. We explore these issues in detail and devise expected points models that account for them. We also introduce a widely applicable novel methodological approach to mitigate overfitting, using a catalytic prior to smooth our machine learning models., Comment: version 0; still have editing to do in the body

Colloidoscope is a deep learning pipeline employing a 3D residual Unet architecture, designed to enhance the tracking of dense colloidal suspensions through confocal microscopy. This methodology uses a simulated training dataset that reflects a wide array of real-world imaging conditions, specifically targeting high colloid volume fraction and low-contrast scenarios where traditional detection methods struggle. Central to our approach is the use of experimental signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and point-spread-functions (PSFs) to accurately quantify and simulate the experimental data. Our findings reveal that Colloidoscope achieves superior recall in particle detection (finds more particles) compared to conventional heuristic methods. Simultaneously, high precision is maintained (high fraction of true positives.) The model demonstrates a notable robustness to photobleached samples, thereby prolonging the imaging time and number of frames than may be acquired. Furthermore, Colloidoscope maintains small scale resolution sufficient to classify local structural motifs. Evaluated across both simulated and experimental datasets, Colloidoscope brings the advancements in computer vision offered by deep learning to particle tracking at high volume fractions. We offer a promising tool for researchers in the soft matter community, this model is deployed and available to use pretrained: https://github.com/wahabk/colloidoscope., Comment: 20 pages

The structure of extended affine Weyl symmetry group of higher Painlev\'e equations of $N$ periodicity depends on whether $N$ is even or odd. We find that for even $N$, the symmetry group ${\widehat A}^{(1)}_{N-1}$ contains the conventional B\"acklund transformations $s_j, j=1,{\ldots},N$, the group of automorphisms consisting of cycling permutations but also reflections on a periodic circle of $N$ points, which is a novel feature uncovered in this paper. The presence of reflection automorphisms is connected to existence of degenerated solutions and for $N=4$ we explicitly show how the reflection automorphisms around even points cause degeneracy of a class of rational solutions obtained on the orbit of translation operators of ${\widehat A}^{(1)}_{3}$. We obtain the closed expressions for solutions and their degenerated counterparts in terms of determinants of Kummer polynomials., Comment: 26 pages

Proton-proton collisions at energy-frontier facilities produce an intense flux of high-energy light particles, including neutrinos, in the forward direction. At the LHC, these particles are currently being studied with the far-forward experiments FASER/FASER$\nu$ and SND@LHC, while new dedicated experiments have been proposed in the context of a Forward Physics Facility (FPF) operating at the HL-LHC. Here we present a first quantitative exploration of the reach for neutrino, QCD, and BSM physics of far-forward experiments integrated within the proposed Future Circular Collider (FCC) project as part of its proton-proton collision program (FCC-hh) at $\sqrt{s} \simeq 100$ TeV. We find that $10^9$ electron/muon neutrinos and $10^7$ tau neutrinos could be detected, an increase of several orders of magnitude compared to (HL-)LHC yields. We study the impact of neutrino DIS measurements at the FPF@FCC to constrain the unpolarised and spin partonic structure of the nucleon and assess their sensitivity to nuclear dynamics down to $x \sim 10^{-9}$ with neutrinos produced in proton-lead collisions. We demonstrate that the FPF@FCC could measure the neutrino charge radius for $\nu_{e}$ and $\nu_\mu$ and reach down to five times the SM value for $\nu_\tau$. We fingerprint the BSM sensitivity of the FPF@FCC for a variety of models, including dark Higgs bosons, relaxion-type scenarios, quirks, and millicharged particles, finding that these experiments would be able to discover LLPs with masses as large as 50 GeV and couplings as small as $10^{-8}$, and quirks with masses up to 10 TeV. Our study highlights the remarkable opportunities made possible by integrating far-forward experiments into the FCC project, and it provides new motivation for the FPF at the HL-LHC as an essential precedent to optimize the forward physics experiments that will enable the FCC to achieve its full physics potential.

Morphological features in galaxies, like spiral arms, bars, rings, tidal tails etc. carry information about their structure, origin and evolution. It is therefore important to catalog and study such features and to correlate them with other basic galaxy properties, the environment in which the galaxies are located and their interactions with other galaxies. The volume of present and future data on galaxies is so large that traditional methods, which involve expert astronomers identifying morphological features through visual inspection, are no longer sufficient. It is therefore necessary to use AI based techniques like machine learning and deep learning for finding morphological structures quickly and efficiently. We report in this study the application of deep learning for finding ring like structures in galaxy images from the Sloan Digital Sky Survey (SDSS) data release DR18. We use a catalog by Buta (2017) of ringed galaxies from the SDSS to train the network, reaching good accuracy and recall, and generate a catalog of 29420 galaxies of which 4855 have ring like structures with prediction confidence exceeding 90 percent. Using a catalog of barred galaxy images identified by Abraham et. al. (2018) using deep learning techniques, we identify a set of 2087 galaxies with bars as well as rings. The catalog should be very useful in understanding the origin of these important morphological structures. As an example of the usefulness of the catalog, we explore the environments and star formation characteristics of ring galaxies in our sample., Comment: 22 pages, 15 figures, 1 Table. Accepted for publication in The Astrophysical Journal (ApJ)

In rare earth nickelates (RENiO$_3$), electron-lattice coupling drives a concurrent metal-to-insulator and bond disproportionation phase transition whose microscopic origin has long been the subject of active debate. Of several proposed mechanisms, here we test the hypothesis that pairs of self-doped ligand holes spatially condense to provide local spin moments that are antiferromagnetically coupled to Ni spins. These singlet-like states provide a basis for long-range bond and spiral spin order. Using magnetic resonant X-ray scattering on NdNiO$_3$ thin films, we observe the chiral nature of the spin-disproportionated state, with spin spirals propagating along the crystallographic (101)$_\mathrm{ortho}$ direction. These spin spirals are found to preferentially couple to X-ray helicity, establishing the presence of a hitherto-unobserved macroscopic chirality. The presence of this chiral magnetic configuration suggests a potential multiferroic coupling between the noncollinear magnetic arrangement and improper ferroelectric behavior as observed in prior studies on NdNiO$_3$ (101)$_\mathrm{ortho}$ films and RENiO$_3$ single crystals. Experimentally constrained theoretical double-cluster calculations confirm the presence of an energetically stable spin-disproportionated state with Zhang-Rice singlet-like combinations of Ni and ligand moments., Comment: 6 pages, 4 figures

One of the main obstacles for the signal extraction of the three point correlation function using photometric surveys, such as the Rubin Observatory Legacy Survey of Space and Time (LSST), will be the prohibitive computation time required for dealing with a vast quantity of sources. Brute force algorithms, which naively scales as $O(N^3)$ with the number of objects, can be further improved with tree methods but not enough to deal with large scale correlations of Rubin's data. However, a harmonic basis decomposition of these higher order statistics reduces the time dramatically, to scale as a two-point correlation function with the number of objects, so that the signal can be extracted in a reasonable amount of time. In this work, we aim to develop the framework to use these expansions within the Limber approximation for scalar (or spin-0) fields, such as galaxy counts, weak lensing convergence or aperture masses. We develop an estimator to extract the signal from catalogs and different phenomenological and theoretical models for its description. The latter includes halo model and standard perturbation theory, to which we add a simple effective field theory prescription based on the short range of non-locality of cosmic fields, significantly improving the agreement with simulated data. In parallel to the modeling of the signal, we develop a code that can efficiently calculate three points correlations of more than 200 million data points (a full sky simulation with Nside=4096) in $\sim$40 minutes, or even less than 10 minutes using an approximation in the searching algorithm, on a single high-performance computing node, enabling a feasible analysis for the upcoming LSST data., Comment: version 2: 31 pages, 10 figures

We use the inferred evolution of Lyman-$\alpha$ luminosities of galaxies in the redshift range of $z \sim 9-16$ from the ${James \ Webb \ Space \ Telescope}$ (JWST) data to predict the power spectrum of Loeb-Rybicki haloes formed by the scattering of Lyman-$\alpha$ photons from neutral hydrogen gas in the intergalactic medium expanding with the Hubble flow, until they Doppler shift out of resonance and escape towards the observer. This leads to the formation of the so-called Loeb-Rybicki intergalactic haloes, which are expected to be prominent even before the epoch of reionization. We find excellent prospects for a statistical detection of the intensity mapping signal from the clustering of these haloes, with current and future experiments such as the JWST, SPHEREx and CDIM. We also describe the detectability of the signal in cross-correlation with the 21-cm emission from the neutral hydrogen in the intergalactic medium at these redshifts. We find that the cross-correlation signal should be detectable at a significance of a few to a few tens of standard deviations out to $z \sim 13$ and marginally out to $z \sim 16$, using the above experiments in combination with the Square Kilometre Array (SKA)-LOW and its pathfinder, the Murchison Widefield Array (MWA)., Comment: 10 pages, 6 figures, 3 tables; v2: clarifications in response to referee comments and discussion on polarization added, main results unchanged

Long-range Rydberg molecules are typically bound in wells formed in their oscillatory potential energy curves. In alkaline Rydberg molecules, bound vibrational states exist even when these potential wells are disrupted by level repulsion from the steep butterfly potential energy curve induced by a scattering shape resonance. The binding in this case is attributed to quantum reflection. However, the rapidly varying regions of the potential energy landscape where quantum reflection occurs often coincide with regions where non-adiabatic coupling becomes significant. By comparing the molecular states calculated within the Born-Oppenheimer approximation, where quantum reflection is the only binding mechanism, with those obtained from the full set of coupled channel equations, we can assess the effects of non-adiabatic coupling on vibrational energies and lifetimes. Our findings show that these couplings can stabilize the molecule by providing an additional barrier which protects the vibrational states from predissociation and non-radiative transitions. There can also be extreme cases where non-adiabatic coupling completely dominates the binding and the molecular lifetimes saturate at the atomic Rydberg lifetime.

The dynamics of domain walls in a square of permalloy (Ni$_{81}$Fe$_{19}$; Py) upon excitation with an oscillating magnetic field of 4 mT amplitude were recorded by pump-probe ptychography with X-ray magnetic circular dichroism (XMCD) at the Ni L$_3$-edge. The 2.5 $\mu$m Py square of 160 nm thickness forms a vortex flux-closure pattern with domain walls that fall into alternating out-of-plane magnetization states due to the interplay of in-plane shape and growth-induced perpendicular anisotropies. Dynamic modes of the domain wall structure were excitable along with the vortex core gyration with frequencies of 500 MHz and 1 GHz. Micromagnetic simulations served to corroborate the imaged domain wall motion.

Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of $\Lambda$ = 2.14 $\pm$ 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% $\pm$ 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit's lifetime by a factor of 2.4 $\pm$ 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 $\mu$s at distance-5 up to a million cycles, with a cycle time of 1.1 $\mu$s. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3 $\times$ 10$^9$ cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms., Comment: 10 pages, 4 figures, Supplementary Information

Today's mainstream network timing models for distributed computing are synchrony, partial synchrony, and asynchrony. These models are coarse-grained and often make either too strong or too weak assumptions about the network. This paper introduces a new timing model called granular synchrony that models the network as a mixture of synchronous, partially synchronous, and asynchronous communication links. The new model is not only theoretically interesting but also more representative of real-world networks. It also serves as a unifying framework where current mainstream models are its special cases. We present necessary and sufficient conditions for solving crash and Byzantine fault-tolerant consensus in granular synchrony. Interestingly, consensus among $n$ parties can be achieved against $f \geq n/2$ crash faults or $f \geq n/3$ Byzantine faults without resorting to full synchrony.

Object detectors are vulnerable to backdoor attacks. In contrast to classifiers, detectors possess unique characteristics, architecturally and in task execution; often operating in challenging conditions, for instance, detecting traffic signs in autonomous cars. But, our knowledge dominates attacks against classifiers and tests in the "digital domain". To address this critical gap, we conducted an extensive empirical study targeting multiple detector architectures and two challenging detection tasks in real-world settings: traffic signs and vehicles. Using the diverse, methodically collected videos captured from driving cars and flying drones, incorporating physical object trigger deployments in authentic scenes, we investigated the viability of physical object-triggered backdoor attacks in application settings. Our findings revealed 8 key insights. Importantly, the prevalent "digital" data poisoning method for injecting backdoors into models does not lead to effective attacks against detectors in the real world, although proven effective in classification tasks. We construct a new, cost-efficient attack method, dubbed MORPHING, incorporating the unique nature of detection tasks; ours is remarkably successful in injecting physical object-triggered backdoors, even capable of poisoning triggers with clean label annotations or invisible triggers without diminishing the success of physical object triggered backdoors. We discovered that the defenses curated are ill-equipped to safeguard detectors against such attacks. To underscore the severity of the threat and foster further research, we, for the first time, release an extensive video test set of real-world backdoor attacks. Our study not only establishes the credibility and seriousness of this threat but also serves as a clarion call to the research community to advance backdoor defenses in the context of object detection., Comment: Accepted to appear at the 40th Annual Computer Security Applications Conference (ACSAC 2024). Code and dataset are available at https://backdoordetectors.github.io/

Current cosmological constraints allow primordial black holes (PBHs) to constitute dark matter in the mass range of $10^{18}$-$10^{22}$g. We show that a major portion of this logarithmic window can be ruled-out based on the Solar System ephemeris, given that the external mass enclosed within 50 au from the Sun did not change by more than $\sim 5\times 10^{-14}$ solar masses per year in recent decades., Comment: Accepted for publication in Research Notes of the American Astronomical Society (August 2024), 4 pages

We show that black holes can be made of light by adding gravity to the discussion of Alvarez-Dominguez et al., arXiv:2405.02389 [PRL 130, 041401 (2024)]., Comment: Comment on arXiv:2405.02389 [PRL 130, 041401 (2024)], 2 pages; submitted for publication as a comment to Physical Review Letters, with added references

Decentralized Health Intelligence Network (DHIN) extends the Decentralized Intelligence Network (DIN) framework to address challenges in healthcare data sovereignty and AI utilization. Building upon DIN's core principles, DHIN introduces healthcare-specific components to tackle data fragmentation across providers and institutions, establishing a sovereign architecture for healthcare provision. It facilitates effective AI utilization by overcoming barriers to accessing diverse health data sources. This comprehensive framework leverages: 1) self-sovereign identity architecture coupled with a personal health record (PHR), extending DIN's personal data stores concept to ensure health data sovereignty; 2) a scalable federated learning (FL) protocol implemented on a public blockchain for decentralized AI training in healthcare, tailored for medical data; and 3) a scalable, trustless rewards mechanism adapted from DIN to incentivize participation in healthcare AI development. DHIN operates on a public blockchain with an immutable record, ensuring that no entity can control access to health data or determine financial benefits. It supports effective AI training while allowing patients to maintain control over their health data, benefit financially, and contribute to a decentralized ecosystem. Unique to DHIN, patients receive rewards in digital wallets as an incentive to opt into the FL protocol, with a long-term roadmap to fund decentralized insurance solutions. This approach introduces a novel, self-financed healthcare model that adapts to individual needs, complements existing systems, and redefines universal coverage, showcasing how DIN principles can transform healthcare data management and AI utilization while empowering patients., Comment: 13 pages, 6 figures

In this paper, we study the mechanical system associated with rolling a Lorentzian manifold $(M,g)$ of dimension $n+1\geq2$ on flat Lorentzian space $\widehat{M}={\mathbb R}^{n,1}$, without slipping or twisting. Using previous results, it is known that there exists a distribution $\mathcal{D}_R$ of rank $(n+1)$ defined on the configuration space $Q(M,\widehat{M})$ of the rolling system, encoding the no-slip and no-twist conditions. Our objective is to study the problem of complete controllability of the control system associated with $\mathcal{D}_R$. The key lies in examining the holonomy group of the distribution $\mathcal{D}_R$ and, following the approach of \cite{ChKok}, establishing that the rolling problem is completely controllable if and only if the holonomy group of $(M,g)$ equals $SO_0(n,1)$., Comment: 13 pages

In recent years, the threat facing airports from growing and increasingly sophisticated cyberattacks has become evident. Airports are considered a strategic national asset, so protecting them from attacks, specifically cyberattacks, is a crucial mission. One way to increase airports' security is by using Digital Twins (DTs). This paper shows and demonstrates how DTs can enhance the security mission. The integration of DTs with Generative AI (GenAI) algorithms can lead to synergy and new frontiers in fighting cyberattacks. The paper exemplifies ways to model cyberattack scenarios using simulations and generate synthetic data for testing defenses. It also discusses how DTs can be used as a crucial tool for vulnerability assessment by identifying weaknesses, prioritizing, and accelerating remediations in case of cyberattacks. Moreover, the paper demonstrates approaches for anomaly detection and threat hunting using Machine Learning (ML) and GenAI algorithms. Additionally, the paper provides impact prediction and recovery coordination methods that can be used by DT operators and stakeholders. It also introduces ways to harness the human factor by integrating training and simulation algorithms with Explainable AI (XAI) into the DT platforms. Lastly, the paper offers future applications and technologies that can be utilized in DT environments.

Classical T Tauri Stars (CTTSs) are highly variable stars that possess gas- and dust-rich disks from which planets form. Much of their variability is driven by mass accretion from the surrounding disk, a process that is still not entirely understood. A multi-epoch optical spectral monitoring campaign of four CTTSs (TW Hya, RU Lup, BP Tau, and GM Aur) was conducted along with contemporaneous HST UV spectra and ground-based photometry in an effort to determine accretion characteristics and gauge variability in this sample. Using an accretion flow model, we find that the magnetospheric truncation radius varies between 2.5-5 R* across all of our observations. There is also significant variability in all emission lines studied, particularly Halpha, Hbeta, and Hgamma. Using previously established relationships between line luminosity and accretion, we find that, on average, most lines reproduce accretion rates consistent with accretion shock modeling of HST spectra to within 0.5 dex. Looking at individual contemporaneous observations, however, these relationships are less accurate, suggesting that variability trends differ from the trends of the population and that these empirical relationships should be used with caution in studies of variability., Comment: 39 pages, 10 figures, 12 tables

Koopman operator theory offers a rigorous treatment of dynamics and has been emerging as a powerful modeling and learning-based control method enabling significant advancements across various domains of robotics. Due to its ability to represent nonlinear dynamics as a linear operator, Koopman theory offers a fresh lens through which to understand and tackle the modeling and control of complex robotic systems. Moreover, it enables incremental updates and is computationally inexpensive making it particularly appealing for real-time applications and online active learning. This review comprehensively presents recent research results on advancing Koopman operator theory across diverse domains of robotics, encompassing aerial, legged, wheeled, underwater, soft, and manipulator robotics. Furthermore, it offers practical tutorials to help new users get started as well as a treatise of more advanced topics leading to an outlook on future directions and open research questions. Taken together, these provide insights into the potential evolution of Koopman theory as applied to the field of robotics.

In this work, we show that exploiting additional variables in a mixed finite element formulation of deformation leads to an efficient physics-based character skinning algorithm. Taking as input, a user-defined rig, we show how to efficiently compute deformations of the character mesh which respect artist-supplied handle positions and orientations, but without requiring complicated constraints on the physics solver, which can cause poor performance. Rather we demonstrate an efficient, user controllable skinning pipeline that can generate compelling character deformations, using a variety of physics material models.

The first interstellar objects, such as 'Oumuamua, Borisov, and IM1, were discovered over the past decade. We follow the trajectories of known interstellar objects in the gravitational potential of the Milky Way galaxy to constrain their possible origin. By initiating the trajectories based on their measured velocities relative to the Local Standard of Rest and considering their vertical excursion from the Milky Way's disk mid-plane, we estimate their likely ages. We find that 'Oumuamua likely originated near the mid-plane of the thin disk, suggesting a younger age. Comet Borisov's trajectory suggests an age similar to the Sun, while IM1's larger excursions suggest an older source. Finally, we project that human-made probes like Voyager 1 and Pioneer 10 will reach the opposite side of the Milky Way disk in approximately 2 Gyr and return to the Solar vicinity before the Sun becomes a red giant., Comment: 11 pages, 18 figures

The gas-phase velocity dispersions in disk galaxies, which trace turbulence in the interstellar medium, are observed to increase with lookback time. However, the mechanisms that set this rise in turbulence are observationally poorly constrained. To address this, we combine kiloparsec-scale ALMA observations of CO(3-2) and CO(4-3) with HST observations of H$\alpha$ to characterize the molecular gas and star formation properties of seven local analogues of main sequence galaxies at $z \sim 1-2$, drawn from the DYNAMO sample. Investigating the ''molecular gas main sequence'' on kpc-scales, we find that galaxies in our sample are more gas-rich than local star-forming galaxies at all disk positions. We measure beam smearing corrected molecular gas velocity dispersions and relate them to the molecular gas and star formation rate surface densities. Despite being relatively nearby ($z \sim 0.1$), DYNAMO galaxies exhibit high velocity dispersions and gas and star formation rate surface densities throughout their disks, when compared to local star forming samples. Comparing these measurements to predictions from star formation theory, we find very good agreements with the latest feedback-regulated star formation models. However, we find that theories which combine gravitational energy dissipation from radial gas transport with feedback over-estimate the observed molecular gas velocity dispersions., Comment: 24 pages, 12 figures, 2 tables, accepted for publication in The Astrophysical Journal

We obtain new progresses about the diameter two property and the Daugavet property in tensor product spaces. Namely, the main results of the paper are: -If $X^*$ has the WODP, then $X\widehat{\otimes}_\varepsilon Y$ has the DD2P for any Banach space $Y$. -If $X$ has the WODP, then $X\widehat{\otimes}_\pi Y$ has the DD2P for any Banach space $Y$. -If $X^*$ and $Y^*$ have the WODP then $X\widehat{\otimes}_\varepsilon Y$ has the Daugavet property. The above improve many results in the literature and establish progresses on some open questions., Comment: 17 pages

Intelligent instruction-following robots capable of improving from autonomously collected experience have the potential to transform robot learning: instead of collecting costly teleoperated demonstration data, large-scale deployment of fleets of robots can quickly collect larger quantities of autonomous data that can collectively improve their performance. However, autonomous improvement requires solving two key problems: (i) fully automating a scalable data collection procedure that can collect diverse and semantically meaningful robot data and (ii) learning from non-optimal, autonomous data with no human annotations. To this end, we propose a novel approach that addresses these challenges, allowing instruction-following policies to improve from autonomously collected data without human supervision. Our framework leverages vision-language models to collect and evaluate semantically meaningful experiences in new environments, and then utilizes a decomposition of instruction following tasks into (semantic) language-conditioned image generation and (non-semantic) goal reaching, which makes it significantly more practical to improve from this autonomously collected data without any human annotations. We carry out extensive experiments in the real world to demonstrate the effectiveness of our approach, and find that in a suite of unseen environments, the robot policy can be improved 2x with autonomously collected data. We open-source the code for our semantic autonomous improvement pipeline, as well as our autonomous dataset of 30.5K trajectories collected across five tabletop environments., Comment: 2024 Conference on Robot Learning (CoRL)

The ability to form diamond electrodes on insulating polycrystalline diamond substrates using single-step laser patterning, and the use of the electrodes as a substrate that supports the adhesion and proliferation of human mesenchymal stem cells (hMSCs) is demonstrated. Laser induced graphitisation results in a conductive amorphous carbon surface, rich in oxygen and nitrogen terminations. This results in an electrode with a high specific capacitance of 182 uF/cm2, a wide water window of 3.25 V, and a low electrochemical impedance of 129 Ohms/cm2 at 1 kHz. The electrodes surface exhibited a good level of biocompatibility with hMSCs, supporting cell adhesion and proliferation. The cells cultured on the electrode displayed significant elongation and alignment along the direction of the laser patterned microgrooves. Because of its favourable electrochemical performance and biocompatibility, the laser-patterned diamond electrodes create a potential for a versatile platform in stem cell therapeutics.

Recently, deep learning has demonstrated promising results in enhancing the accuracy of vulnerability detection and identifying vulnerabilities in software. However, these techniques are still vulnerable to attacks. Adversarial examples can exploit vulnerabilities within deep neural networks, posing a significant threat to system security. This study showcases the susceptibility of deep learning models to adversarial attacks, which can achieve 100% attack success rate (refer to Table 5). The proposed method, EaTVul, encompasses six stages: identification of important samples using support vector machines, identification of important features using the attention mechanism, generation of adversarial data based on these features using ChatGPT, preparation of an adversarial attack pool, selection of seed data using a fuzzy genetic algorithm, and the execution of an evasion attack. Extensive experiments demonstrate the effectiveness of EaTVul, achieving an attack success rate of more than 83% when the snippet size is greater than 2. Furthermore, in most cases with a snippet size of 4, EaTVul achieves a 100% attack success rate. The findings of this research emphasize the necessity of robust defenses against adversarial attacks in software vulnerability detection.

We introduce a unique semantic segmentation dataset of 6,096 high-resolution aerial images capturing indigenous and invasive grass species in Bega Valley, New South Wales, Australia, designed to address the underrepresented domain of ecological data in the computer vision community. This dataset presents a challenging task due to the overlap and distribution of grass species, which is critical for advancing models in ecological and agronomical applications. Our study features a homotopy-based multi-objective fine-tuning approach that balances segmentation accuracy and contextual consistency, applicable to various models. By integrating DiceCELoss for pixel-wise classification and a smoothness loss for spatial coherence, this method evolves during training to enhance robustness against noisy data. Performance baselines are established through a case study on the Segment Anything Model (SAM), demonstrating its effectiveness. Our annotation methodology, emphasizing pen size, zoom control, and memory management, ensures high-quality dataset creation. The dataset and code will be made publicly available, aiming to drive research in computer vision, machine learning, and ecological studies, advancing environmental monitoring and sustainable development.

The majority of low-mass ($\log_{10} M_*/M_{\odot}=9-10$) galaxies at high redshift ($z>1$) appear elongated in projection. We use JWST-CEERS observations to explore the role of gravitational lensing in this puzzle. The typical galaxy-galaxy lensing shear $\gamma\sim1\%$ is too low to explain the predominance of elongated early galaxies with ellipticity $e\approx0.6$. However, non-parametric quantile regression with Bayesian Additive Regression Trees reveals hints of an excess of tangentially-aligned source-lens pairs with $\gamma>10\%$. On larger scales, we also find evidence for weak lensing shear. We rule out the null hypothesis of randomly oriented galaxies at $\gtrsim99\%$ significance in multiple NIRCam chips, modules and pointings. The number of such regions is small and attributable to chance, but coherent alignment patterns suggest otherwise. On the chip scale, the average complex ellipticity $\langle e\rangle\sim10\%$ is non-negligible and beyond the level of our PSF uncertainties. The shear variance $\langle\overline{\gamma}^2\rangle\sim10^{-3}$ is an order of magnitude above the conventional weak lensing regime but is more sensitive to PSF systematics, intrinsic alignments, cosmic variance and other biases. Taking it as an upper limit, the maximum implied ``cosmic shear'' is only a few percent and cannot explain the elongated shapes of early galaxies. The alignments themselves may arise from lensing by a protocluster or filament at $z\sim0.75$ where we find an overabundance of massive lens galaxies. We recommend a weak lensing search for overdensities in ``blank'' deep fields with JWST and the Roman Space Telescope., Comment: Submitted to ApJ, main body is 26 pages with 18 figures, comments welcome

This paper presents some preliminary results concerning a new user-friendly operating system interface based on the relational data model that is currently under development at the University of Texas at Austin. The premise of our work is that a relational model of the operating system environment wil produce a user and programmer interface to the system: is easier to use, is easier to learn, and allows greater portability as compared with existing operating system interfaces. Our approach is to model elements of the operating system environment as relations and to model operating system commands as statements in a relational language. In adapting the relational model to an operating system environment, we found it necessary to extend the model and improve existing relational languages. The extensions to the relational model are designed to allow a more natural representation of elements of the environment. Our language extensions exploit the universal relation model and utilize the graphical capabilities of modern workstations. The nature of our investigations is ranging from practical implementation issues to the more theoretical questions of modeling and language semanties.

Thanks to its low or negative surface electron affinity and chemical inertness, diamond is attracting broad attention as a source material of solvated electrons produced by optical excitation of the solid-liquid interface. Unfortunately, its wide bandgap typically imposes the use of wavelengths in the ultra-violet range, hence complicating practical applications. Here we probe the photocurrent response of water surrounded by single-crystal diamond surfaces engineered to host shallow nitrogen-vacancy (NV) centers. We observe clear signatures of diamond-induced photocurrent generation throughout the visible range and for wavelengths reaching up to 594 nm. Experiments as a function of laser power suggest that NV centers and other co-existing defects - likely in the form of surface traps - contribute to carrier injection, though we find that NVs dominate the system response in the limit of high illumination intensities. Given our growing understanding of near-surface NV centers and adjacent point defects, these results open new perspectives in the application of diamond-liquid interfaces to photo-carrier-initiated chemical and spin processes in fluids.

With the growing access to administrative health databases, retrospective studies have become crucial evidence for medical treatments. Yet, non-randomized studies frequently face selection biases, requiring mitigation strategies. Propensity score matching (PSM) addresses these biases by selecting comparable populations, allowing for analysis without further methodological constraints. However, PSM has several drawbacks. Different matching methods can produce significantly different Average Treatment Effects (ATE) for the same task, even when meeting all validation criteria. To prevent cherry-picking the best method, public authorities must involve field experts and engage in extensive discussions with researchers. To address this issue, we introduce a novel metric, A2A, to reduce the number of valid matches. A2A constructs artificial matching tasks that mirror the original ones but with known outcomes, assessing each matching method's performance comprehensively from propensity estimation to ATE estimation. When combined with Standardized Mean Difference, A2A enhances the precision of model selection, resulting in a reduction of up to 50% in ATE estimation errors across synthetic tasks and up to 90% in predicted ATE variability across both synthetic and real-world datasets. To our knowledge, A2A is the first metric capable of evaluating outcome correction accuracy using covariates not involved in selection. Computing A2A requires solving hundreds of PSMs, we therefore automate all manual steps of the PSM pipeline. We integrate PSM methods from Python and R, our automated pipeline, a new metric, and reproducible experiments into popmatch, our new Python package, to enhance reproducibility and accessibility to bias correction methods., Comment: ECML PKDD 2024, 18 pages, 2 figures, 5 tables

Pattern separation is a computational process by which dissimilar neural patterns are generated from similar input patterns. We present an information-geometric formulation of pattern separation, where a pattern separator is modelled as a family of statistical distributions on a manifold. Such a manifold maps an input (i.e. coordinates) to a probability distribution that generates firing patterns. Pattern separation occurs when small coordinate changes result in large distances between samples from the corresponding distributions. Under this formulation, we implement a two-neuron system whose probability law forms a 3-dimensional manifold with mutually orthogonal coordinates representing the neurons' marginal and correlational firing rates. We use this highly controlled system to examine the behaviour of spike train similarity indices commonly used in pattern separation research. We found that all indices (except scaling factor) were sensitive to relative differences in marginal firing rates, but no index adequately captured differences in spike trains that resulted from altering the correlation in activity between the two neurons. That is, existing pattern separation metrics appear (A) sensitive to patterns that are encoded by different neurons, but (B) insensitive to patterns that differ only in relative spike timing (e.g. synchrony between neurons in the ensemble).

Recently, a new population of compact, high-redshift (z>7) galaxies appeared as little red dots (LRDs) in deep JWST observations. The latest spectroscopic data indicates that these galaxies contain an evolved stellar population, reflecting an early episode of high star-formation-rate. The appearance of broad emission lines suggests that a central overmassive black hole also powers these galaxies. I propose that LRD galaxies represent the low-spin tail of the galaxy population. Low-spin galaxies host a more compact gaseous disk with an enhanced star formation rate relative to typical galaxies at the same redshift. The compact disk feeds efficiently a central black hole, as predicted by Eisenstein & Loeb (1995)., Comment: Published in RNAAS, 4 pages

This paper examines the relationship between solar activity and extreme rainfall events in Kerala, India. Kerala receives minimum and maximum rainfall during winter and monsoon seasons. Sunspot number, F10.7 Index, and cosmic ray intensity are the solar indices considered, and their variations with rainfall are studied over 57 years (1965-2021), i.e., starting from Solar Cycle 20. Correlative studies are performed for each solar cycle, and correlation coefficients are calculated. We find that rainfall in Kerala is correlated with sunspot activity but with varying degrees of significance. During Solar Cycle 21, rainfall and solar activity are correlated with high significance during both winter and monsoon seasons. The variation of different solar indices with rainfall is studied. The winter season showed a better link between the sun and rainfall than the monsoon season. The years with rainfall surplus and deficiency are calculated and compared with the solar indices. {We find that the years with rainfall excess and deficit mainly occur in the years around solar maximum or minimum (within $\pm$2 years).} We hypothesize a physical relationship between solar activity and extreme rainfall events in Kerala that contributes to their predictability., Comment: 16 pages, 10 figures; (Earlier version available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4596139)

In this paper we analyse when every element of $X\widehat{\otimes}_\pi Y$ attains its projective norm. We prove that this is the case if $X$ is the dual of a subspace of a predual of an $\ell_1(I)$ space and $Y$ is $1$-complemented in its bidual under approximation properties assumptions. This result allows us to provide some new examples where $X$ is a Lipschitz-free space. We also prove that the set of norm-attaining elements is dense in $X\widehat{\otimes}_\pi Y$ if, for instance, $X=L_1(\mu)$ and $Y$ is any Banach space, or if $X$ has the metric $\pi$-property and $Y$ is a dual space with the RNP.

Large Language Models have recently been applied to text annotation tasks from social sciences, equalling or surpassing the performance of human workers at a fraction of the cost. However, no inquiry has yet been made on the impact of prompt selection on labelling accuracy. In this study, we show that performance greatly varies between prompts, and we apply the method of automatic prompt optimization to systematically craft high quality prompts. We also provide the community with a simple, browser-based implementation of the method at https://prompt-ultra.github.io/ .

Continuous-variable projective measurements can not select individual measurement results as in the discrete case; instead, the possible outcomes are bounded by the selectivity interval of the measurement; then, it is say that continuous-variable measurement devices are insufficiently selective. By utilizing this concept we show that the probability and fidelity of teleportation in a two-mode cluster state can be handled by the localization of the selectivity interval of the measurement apparatus. Besides, we provide a mathematical expression describing the probability distribution of the measurement outcomes in the two-mode cluster, which is a fundamental solution of the heat equation. In addition, we show that the fidelity of teleportation in the two mode cluster is given by the quotient between the squared solution of a non-homogeneous heat equation and the solution of the conventional heat equation. Furthermore, we extend our approach to a configuration involving successive clusters with intermediate corrections between each teleportation step. To exemplify our proposal, we consider the specific case of a squeezed-coherent state as the quantum state under teleportation., Comment: 26 pages, 8 figures

Flood prediction is a critical challenge in the context of climate change, with significant implications for ecosystem preservation, human safety, and infrastructure protection. In this study, we tackle this problem by applying the Quantum-Train (QT) technique to a forecasting Long Short-Term Memory (LSTM) model trained by Quantum Machine Learning (QML) with significant parameter reduction. The QT technique, originally successful in the A Matter of Taste challenge at QHack 2024, leverages QML to reduce the number of trainable parameters to a polylogarithmic function of the number of parameters in a classical neural network (NN). This innovative framework maps classical NN weights to a Hilbert space, altering quantum state probability distributions to adjust NN parameters. Our approach directly processes classical data without the need for quantum embedding and operates independently of quantum computing resources post-training, making it highly practical and accessible for real-world flood prediction applications. This model aims to improve the efficiency of flood forecasts, ultimately contributing to better disaster preparedness and response., Comment: 6 pages, 4 figures

NOTT (formerly Hi-5) is the L'-band (3.5-4.0~microns) nulling interferometer of Asgard, an instrument suite in preparation for the VLTI visitor focus. The primary scientific objectives of NOTT include characterizing (i) young planetary systems near the snow line, a critical region for giant planet formation, and (ii) nearby main-sequence stars close to the habitable zone, with a focus on detecting exozodiacal dust that could obscure Earth-like planets. In 2023-2024, the final warm optics have been procured and assembled in a new laboratory at KU Leuven. First fringes and null measurements were obtained using a Gallium Lanthanum Sulfide (GLS) photonic chip that was also tested at cryogenic temperatures. In this paper, we present an overall update of the NOTT project with a particular focus on the cold mechanical design, the first results in the laboratory with the final NOTT warm optics, and the ongoing Asgard integration activities. We also report on other ongoing activities such as the characterization of the photonic chip (GLS, LiNbO3, SiO), the development of the exoplanet science case, the design of the dispersion control module, and the progress with the self-calibration data reduction software., Comment: 11 pages (incl. 5 figures); Proc. SPIE Astronomical Telescopes + Instrumentation 2024 (Yokohama; Japan), Optical and Infrared Interferometry and Imaging VI

Quantum reinforcement learning utilizes quantum layers to process information within a machine learning model. However, both pure and hybrid quantum reinforcement learning face challenges such as data encoding and the use of quantum computers during the inference stage. We apply the Quantum-Train method to reinforcement learning tasks, called QTRL, training the classical policy network model using a quantum machine learning model with polylogarithmic parameter reduction. This QTRL approach eliminates the data encoding issues of conventional quantum machine learning and reduces the training parameters of the corresponding classical policy network. Most importantly, the training result of the QTRL is a classical model, meaning the inference stage only requires classical computer. This is extremely practical and cost-efficient for reinforcement learning tasks, where low-latency feedback from the policy model is essential., Comment: 6 pages, 1 figure

The Dragonfly Telephoto Array employs a unique design to detect very large and diffuse galaxies, which might be missed with conventional telescopes. The Dragonfly Ultrawide Survey (DFUWS) is a new wide-field survey which will cover 10,000 deg$^2$ of the northern sky, and it provides an ideal dataset to find these large diffuse galaxies. From 3100 deg$^2$ of DFUWS data, we identified eleven large, low surface brightness galaxies as a pilot sample for spectroscopic follow-up. These are the largest galaxies in the examined area that appear smooth and isolated, with effective radii of 12"-27". Eight are below 24 $\mathrm{mag\,arcsec^{-2}}$ in central $g$-band surface brightness. Keck Cosmic Web Imager (KCWI) spectra of the diffuse light show that all eleven galaxies in this sample are quiescent, and seven qualify as ultra-diffuse galaxies (UDGs). Eight galaxies have distances between 15 and 30 Mpc, while the other three are in the Pegasus cluster at 50 Mpc. Their spectra show evidence of a $\sim 1$Gyr old stellar population in addition to an even older stellar population. The intermediate-age component is present in group and satellite galaxies but not in the Pegasus cluster UDGs. All galaxies in this sample are detected in both Dragonfly and Legacy imaging, and the sample partially overlaps with existing UDG catalogs. This pilot sample provides an excellent training set for our analysis of the upcoming full 10,000 deg$^2$ DFUWS data, from which we may expect to discover even larger, previously-unknown galaxies., Comment: Submitted to ApJ

Model Predictive Control (MPC) is an optimal control strategy suited for flood control of water resources infrastructure. Despite many studies on reservoir flood control and their theoretical contribution, optimisation methodologies have not been widely applied in real-time operation due to disparities between research assumptions and practical requirements. First, tacit objectives such as minimising the magnitude and frequency of changes in the existing outflow schedule are considered important in practice, but these are nonlinear and challenging to formulate to suit all conditions. Incorporating these objectives transforms the problem into a multi-objective nonlinear optimisation problem that is difficult to solve online. Second, it is reasonable to assume that the weights and parameters are not stationary because the preference varies depending on the state of the system. To overcome these limitations, we propose a framework that converts the original intractable problem into parameterized linear MPC problems with dynamic optimisation of weights and parameters. This is done by introducing a model-based learning concept under the assumption of the dynamic nature of the operator's preference. We refer to this framework as Parameterised Dynamic MPC (PD-MPC). The effectiveness of this framework is demonstrated through a numerical experiment for the Daecheong multipurpose reservoir in South Korea. We find that PD-MPC outperforms `standard' MPC-based designs without a dynamic optimisation process under the same uncertain inflows.

Decentralized Intelligence Network (DIN) is a theoretical framework designed to address challenges in AI development, particularly focusing on data fragmentation and siloing issues. It facilitates effective AI training within sovereign data networks by overcoming barriers to accessing diverse data sources, leveraging: 1) personal data stores to ensure data sovereignty, where data remains securely within Participants' control; 2) a scalable federated learning protocol implemented on a public blockchain for decentralized AI training, where only model parameter updates are shared, keeping data within the personal data stores; and 3) a scalable, trustless cryptographic rewards mechanism on a public blockchain to incentivize participation and ensure fair reward distribution through a decentralized auditing protocol. This approach guarantees that no entity can prevent or control access to training data or influence financial benefits, as coordination and reward distribution are managed on the public blockchain with an immutable record. The framework supports effective AI training by allowing Participants to maintain control over their data, benefit financially, and contribute to a decentralized, scalable ecosystem that leverages collective AI to develop beneficial algorithms., Comment: 16 pages, 1 figure. DIN was presented by the author as a speaker at the Summit on Responsible Decentralized Intelligence - Future of Decentralization and AI, hosted by Berkeley RDI on August 6, 2024, at the Verizon Center, Cornell Tech Campus, Roosevelt Island, NYC

Greenberger-Horne-Zeilinger (GHZ) states are quantum states involving at least three entangled particles. They are of fundamental interest in quantum information theory, and the construction of such states of high dimension has various applications in quantum communication and cryptography. They are of fundamental interest in quantum information theory, and the construction of such states of high dimension has various applications in quantum communication and cryptography. Krenn, Gu and Zeilinger discovered a correspondence between a large class of quantum optical experiments which produce GHZ states and edge-weighted edge-coloured multi-graphs with some special properties called the \emph{GHZ graphs}. On such GHZ graphs, a graph parameter called \emph{dimension} can be defined, which is the same as the dimension of the GHZ state produced by the corresponding experiment. Krenn and Gu conjectured that the dimension of any GHZ graph with more than $4$ vertices is at most $2$. An affirmative resolution of the Krenn-Gu conjecture has implications for quantum resource theory. On the other hand, the construction of a GHZ graph on a large number of vertices with a high dimension would lead to breakthrough results. In this paper, we study the existence of GHZ graphs from the perspective of the Krenn-Gu conjecture and show that the conjecture is true for graphs of vertex connectivity at most 2 and for cubic graphs. We also show that the minimal counterexample to the conjecture should be $4$-connected. Such information could be of great help in the search for GHZ graphs using existing tools like PyTheus. While the impact of the work is in quantum physics, the techniques in this paper are purely combinatorial, and no background in quantum physics is required to understand them., Comment: To appear in MFCS 2024