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We apply the methods of ergodic theory to both simplify and significantly extend some classical results due to Stewart, Tijdeman, and Ruzsa. One of the novel features of our approach is the utilization of pointwise ergodic theory., Comment: 33 pages

Effective intracellular delivery is essential for successful gene editing of cells. Spatially selective delivery to cells that is simultaneously precise, consistent, and non-destructive remains challenging using conventional state-of-the-art techniques. Here, we introduce a carrier-free method for spatiotemporal delivery of fluorescently labeled cargo into both adherent and suspension cells using carbon-black-embedded polydimethylsiloxane (PDMS) substrates irradiated by nanosecond laser pulses. This low-cost, biocompatible material, coupled with an optical approach, enables scalable, spatially selective, and sequential delivery of multiple cargo molecules, including FITC-dextran and siRNA, to a broad range of cells. Notably, we achieved siRNA delivery into the cytoplasm of hard-to-transfect K562 cells with 45% efficiency, while maintaining nearly 100% cell viability.

The idea of 3D reconstruction as scene understanding is foundational in computer vision. Reconstructing 3D scenes from 2D visual observations requires strong priors to disambiguate structure. Much work has been focused on the anthropocentric, which, characterized by smooth surfaces, coherent normals, and regular edges, allows for the integration of strong geometric inductive biases. Here, we consider a more challenging problem where such assumptions do not hold: the reconstruction of natural scenes containing trees, bushes, boulders, and animals. While numerous works have attempted to tackle the problem of reconstructing animals in the wild, they have focused solely on the animal, neglecting environmental context. This limits their usefulness for analysis tasks, as animals exist inherently within the 3D world, and information is lost when environmental factors are disregarded. We propose a method to reconstruct natural scenes from single images. We base our approach on recent advances leveraging the strong world priors ingrained in Large Language Models and train an autoregressive model to decode a CLIP embedding into a structured compositional scene representation, encompassing both animals and the wild (RAW). To enable this, we propose a synthetic dataset comprising one million images and thousands of assets. Our approach, having been trained solely on synthetic data, generalizes to the task of reconstructing animals and their environments in real-world images. We will release our dataset and code to encourage future research at https://raw.is.tue.mpg.de/, Comment: 12 pages; project page: https://raw.is.tue.mpg.de/

We consider an initial-boundary value problem for the chemotaxis-Navier--Stokes system \begin{align*} \left\{ \begin{array}{c@{\quad}l@{\quad}l@{\,}c} n_{t}+u\cdot\nabla n=\nabla\cdot\big(D(n)\nabla n-nS(x,n,c)\cdot\nabla c\big),\ &x\in\Omega,& t>0,\\ c_{t}+u\cdot\nabla c=\Delta c-cn,\ &x\in\Omega,& t>0,\\ u_{t}+(u\cdot\nabla)u=\Delta u+\nabla P+n\nabla\Phi,\quad \nabla\cdot u=0,\ &x\in\Omega,& t>0,\\ \big(D(n)\nabla n-nS(x,n,c)\cdot\nabla c)\cdot\nu=\nabla c\cdot\nu=0,\ u=0,\ &x\in\partial\Omega,& t>0,\\ n(\cdot,0)=n_0,\ c(\cdot,0)=c_0,\ u(\cdot,0)=u_0,\ &x\in\Omega. \end{array}\right. \end{align*} in a smoothly bounded domain $\Omega\subset\mathbb{R}^2$. Assuming $S:\overline{\Omega}\times[0,\infty)\times(0,\infty)\rightarrow \mathbb{R}^{2\times 2}$ to be sufficiently regular and such that with $\gamma\in[0,\frac56]$ and some non-decreasing $S_0:(0,\infty)\to(0,\infty)$, we have \begin{align*} \big|S(x,n,c)\big|\leq \frac{S_0(c)}{c^\gamma}\quad\text{for all }(x,n,c)\in\overline{\Omega}\times[0,\infty)\times(0,\infty), \end{align*} we show that if $D:[0,\infty)\to[0,\infty)$ is suitably regular and positive throughout $(0,\infty)$, then for all $M>0$ one can find $L(M)>0$ such that whenever $$\liminf_{n\to\infty} D(n)>L\quad\text{and}\quad \liminf_{n\searrow0}\frac{D(n)}{n}>0$$ are satisfied and the initial data $(n_0,c_0,u_0)$ are suitably regular and satisfy $\|c_0\|_{L^{\infty}(\Omega)}\leq M$ there is a global and bounded weak solution for the initial-boundary value problem above. Under the additional assumption of $D(0)>0$ this solution is moreover a classical solution of the same problem., Comment: 34 pages

We demonstrate the suitability of high dynamic range, high-speed, neuromorphic event-based, dynamic vision sensors for metallic additive manufacturing and welding for in-process monitoring applications. In-process monitoring to enable quality control of mission critical components produced using metallic additive manufacturing is of high interest. However, the extreme light environment and high speed dynamics of metallic melt pools have made this a difficult environment in which to make measurements. Event-based sensing is an alternative measurement paradigm where data is only transmitted/recorded when a measured quantity exceeds a threshold resolution. The result is that event-based sensors consume less power and less memory/bandwidth, and they operate across a wide range of timescales and dynamic ranges. Event-driven driven imagers stand out from conventional imager technology in that they have a very high dynamic range of approximately 120 dB. Conventional 8 bit imagers only have a dynamic range of about 48 dB. This high dynamic range makes them a good candidate for monitoring manufacturing processes that feature high intensity light sources/generation such as metallic additive manufacturing and welding. In addition event based imagers are able to capture data at timescales on the order of 100 {\mu}s, which makes them attractive to capturing fast dynamics in a metallic melt pool. In this work we demonstrate that event-driven imagers have been shown to be able to observe tungsten inert gas (TIG) and laser welding melt pools. The results of this effort suggest that with additional engineering effort, neuromorphic event imagers should be capable of 3D geometry measurements of the melt pool, and anomaly detection/classification/prediction., Comment: This work is a derivative work of a conference proceedings paper submitted to the International Modal Analysis Conference 2024, and is subject to some copyright restrictions associated with the Society of Experimental Mechanics. A variation of this paper is also published in the Weapons Engineering Symposium and Journal (WESJ) which is not publically accessible

Path Planning for stochastic hybrid systems presents a unique challenge of predicting distributions of future states subject to a state-dependent dynamics switching function. In this work, we propose a variant of Model Predictive Path Integral Control (MPPI) to plan kinodynamic paths for such systems. Monte Carlo may be inaccurate when few samples are chosen to predict future states under state-dependent disturbances. We employ recently proposed Unscented Transform-based methods to capture stochasticity in the states as well as the state-dependent switching surfaces. This is in contrast to previous works that perform switching based only on the mean of predicted states. We focus our motion planning application on the navigation of a mobile robot in the presence of dynamically moving agents whose responses are based on sensor-constrained attention zones. We evaluate our framework on a simulated mobile robot and show faster convergence to a goal without collisions when the robot exploits the hybrid human dynamics versus when it does not.

Training methods to perform robust 3D human pose and shape (HPS) estimation requires diverse training images with accurate ground truth. While BEDLAM demonstrates the potential of traditional procedural graphics to generate such data, the training images are clearly synthetic. In contrast, generative image models produce highly realistic images but without ground truth. Putting these methods together seems straightforward: use a generative model with the body ground truth as controlling signal. However, we find that, the more realistic the generated images, the more they deviate from the ground truth, making them inappropriate for training and evaluation. Enhancements of realistic details, such as clothing and facial expressions, can lead to subtle yet significant deviations from the ground truth, potentially misleading training models. We empirically verify that this misalignment causes the accuracy of HPS networks to decline when trained with generated images. To address this, we design a controllable synthesis method that effectively balances image realism with precise ground truth. We use this to create the Generative BEDLAM (Gen-B) dataset, which improves the realism of the existing synthetic BEDLAM dataset while preserving ground truth accuracy. We perform extensive experiments, with various noise-conditioning strategies, to evaluate the tradeoff between visual realism and HPS accuracy. We show, for the first time, that generative image models can be controlled by traditional graphics methods to produce training data that increases the accuracy of HPS methods.

We address the challenge of accurate 3D human pose and shape estimation from monocular images. The key to accuracy and robustness lies in high-quality training data. Existing training datasets containing real images with pseudo ground truth (pGT) use SMPLify to fit SMPL to sparse 2D joint locations, assuming a simplified camera with default intrinsics. We make two contributions that improve pGT accuracy. First, to estimate camera intrinsics, we develop a field-of-view prediction model (HumanFoV) trained on a dataset of images containing people. We use the estimated intrinsics to enhance the 4D-Humans dataset by incorporating a full perspective camera model during SMPLify fitting. Second, 2D joints provide limited constraints on 3D body shape, resulting in average-looking bodies. To address this, we use the BEDLAM dataset to train a dense surface keypoint detector. We apply this detector to the 4D-Humans dataset and modify SMPLify to fit the detected keypoints, resulting in significantly more realistic body shapes. Finally, we upgrade the HMR2.0 architecture to include the estimated camera parameters. We iterate model training and SMPLify fitting initialized with the previously trained model. This leads to more accurate pGT and a new model, CameraHMR, with state-of-the-art accuracy. Code and pGT are available for research purposes., Comment: 3DV 2025

Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or trans-national scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by leveraging and amplifying AI for national-scale collective intelligence. The development and deployment of this kind of AI faces distinctive challenges, both technical and socio-technical. Here, a research strategy for mobilising inter-disciplinary research to address these challenges is detailed and some of the key issues that must be faced are outlined., Comment: 25 pages, 3 figures, Accepted for publication at Knowledge Engineering Review (KER)

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

Touch is a crucial sensing modality that provides rich information about object properties and interactions with the physical environment. Humans and robots both benefit from using touch to perceive and interact with the surrounding environment (Johansson and Flanagan, 2009; Li et al., 2020; Calandra et al., 2017). However, no existing systems provide rich, multi-modal digital touch-sensing capabilities through a hemispherical compliant embodiment. Here, we describe several conceptual and technological innovations to improve the digitization of touch. These advances are embodied in an artificial finger-shaped sensor with advanced sensing capabilities. Significantly, this fingertip contains high-resolution sensors (~8.3 million taxels) that respond to omnidirectional touch, capture multi-modal signals, and use on-device artificial intelligence to process the data in real time. Evaluations show that the artificial fingertip can resolve spatial features as small as 7 um, sense normal and shear forces with a resolution of 1.01 mN and 1.27 mN, respectively, perceive vibrations up to 10 kHz, sense heat, and even sense odor. Furthermore, it embeds an on-device AI neural network accelerator that acts as a peripheral nervous system on a robot and mimics the reflex arc found in humans. These results demonstrate the possibility of digitizing touch with superhuman performance. The implications are profound, and we anticipate potential applications in robotics (industrial, medical, agricultural, and consumer-level), virtual reality and telepresence, prosthetics, and e-commerce. Toward digitizing touch at scale, we open-source a modular platform to facilitate future research on the nature of touch., Comment: 28 pages

Robot learning holds tremendous promise to unlock the full potential of flexible, general, and dexterous robot systems, as well as to address some of the deepest questions in artificial intelligence. However, bringing robot learning to the level of generality required for effective real-world systems faces major obstacles in terms of data, generalization, and robustness. In this paper, we discuss how generalist robot policies (i.e., robot foundation models) can address these challenges, and how we can design effective generalist robot policies for complex and highly dexterous tasks. We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We then discuss how this model can be trained on a large and diverse dataset from multiple dexterous robot platforms, including single-arm robots, dual-arm robots, and mobile manipulators. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people and from a high-level VLM policy, and its ability to acquire new skills via fine-tuning. Our results cover a wide variety of tasks, such as laundry folding, table cleaning, and assembling boxes., Comment: See project website for videos: https://physicalintelligence.company/blog/pi0

Fairness in Multi-Agent Systems (MAS) has been extensively studied, particularly in reward distribution among agents in scenarios such as goods allocation, resource division, lotteries, and bargaining systems. Fairness in MAS depends on various factors, including the system's governing rules, the behaviour of the agents, and their characteristics. Yet, fairness in human society often involves evaluating disparities between disadvantaged and privileged groups, guided by principles of Equality, Diversity, and Inclusion (EDI). Taking inspiration from the work on algorithmic fairness, which addresses bias in machine learning-based decision-making, we define protected attributes for MAS as characteristics that should not disadvantage an agent in terms of its expected rewards. We adapt fairness metrics from the algorithmic fairness literature -- namely, demographic parity, counterfactual fairness, and conditional statistical parity -- to the multi-agent setting, where self-interested agents interact within an environment. These metrics allow us to evaluate the fairness of MAS, with the ultimate aim of designing MAS that do not disadvantage agents based on protected attributes.

This work presents a novel lattice-based methodology for incorporating multidimensional constraints into continuous decision variables within a genetic algorithm (GA) framework. The proposed approach consolidates established transcription techniques for crossover of continuous decision variables, aiming to leverage domain knowledge and guide the search process towards feasible regions of the design space. This work offers a robust and general purpose lattice-based GA that is applicable to a broad range of optimization problems. Monte Carlo analysis demonstrates that lattice-based methods find solutions two orders of magnitude closer to optima in fewer generations. The effectiveness of the lattice-based approach is showcased through two illustrative multi-objective design problems: (1) optimal telescope placement for astrophotography and (2) optimal design of a satellite constellation for maximizing ground station access. The optimal telescope placement example shows that lattice-based methods converge to the Pareto front in 15% fewer generations than traditional methods. The orbit design example shows that lattice-based methods discover an order of magnitude more Pareto-optimal solutions than traditional methods in a highly constrained design space. Overall, the results show that the lattice-based method exhibits enhanced exploration capabilities, traversing the solution space more comprehensively and achieving faster convergence compared to conventional GAs.

Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree systems is a difficult problem. In this work, we propose the Robust Policy CBF (RPCBF), a practical method of constructing CBF approximations that is easy to implement and robust to disturbances via the estimation of a value function. We demonstrate the effectiveness of our method in simulation on a variety of high relative degree input-constrained systems. Finally, we demonstrate the benefits of RPCBF in compensating for model errors on a hardware quadcopter platform by treating the model errors as disturbances. The project page can be found at https://oswinso.xyz/rpcbf., Comment: Submitted to ICRA 2025. The project page can be found at https://oswinso.xyz/rpcbf

Recent work introduced the concept of human teleoperation (HT), where the remote robot typically considered in conventional bilateral teleoperation is replaced by a novice person wearing a mixed reality head mounted display and tracking the motion of a virtual tool controlled by an expert. HT has advantages in cost, complexity, and patient acceptance for telemedicine in low-resource communities or remote locations. However, the stability, transparency, and performance of bilateral HT are unexplored. In this paper, we therefore develop a mathematical model and simulation of the HT system using test data. We then analyze various control architectures with this model and implement them with the HT system to find the achievable performance, investigate stability, and determine the most promising teleoperation scheme in the presence of time delays. We show that instability in HT, while not destructive or dangerous, makes the system impossible to use. However, stable and transparent teleoperation are possible with small time delays (<200 ms) through 3-channel teleoperation, or with large time delays through model-mediated teleoperation with local pose and force feedback for the novice.

Syllables are compositional units of spoken language that play a crucial role in human speech perception and production. However, current neural speech representations lack structure, resulting in dense token sequences that are costly to process. To bridge this gap, we propose a new model, Sylber, that produces speech representations with clean and robust syllabic structure. Specifically, we propose a self-supervised model that regresses features on syllabic segments distilled from a teacher model which is an exponential moving average of the model in training. This results in a highly structured representation of speech features, offering three key benefits: 1) a fast, linear-time syllable segmentation algorithm, 2) efficient syllabic tokenization with an average of 4.27 tokens per second, and 3) syllabic units better suited for lexical and syntactic understanding. We also train token-to-speech generative models with our syllabic units and show that fully intelligible speech can be reconstructed from these tokens. Lastly, we observe that categorical perception, a linguistic phenomenon of speech perception, emerges naturally in our model, making the embedding space more categorical and sparse than previous self-supervised learning approaches. Together, we present a novel self-supervised approach for representing speech as syllables, with significant potential for efficient speech tokenization and spoken language modeling.

We establish the H\"older continuity of bounded nonnegative weak solutions to \begin{align*} \big(\Phi^{-1}(w)\big)_t=\Delta w+\nabla\cdot\big(a(x,t)\Phi^{-1}(w)\big)+b\big(x,t,\Phi^{-1}(w)\big), \end{align*} with convex $\Phi\in C^0([0,\infty))\cap C^2((0,\infty))$ satisfying $\Phi(0)=0$, $\Phi'>0$ on $(0,\infty)$ and $$s\Phi''(s)\leq C\Phi'(s)\quad\text{for all }s\in[0,s_0]$$ for some $C>0$ and $s_0\in(0,1]$. The functions $a$ and $b$ are only assumed to satisfy integrability conditions of the form \begin{align*} a&\in L^{2q_1}\big((0,T);L^{2q_2}(\Omega;\mathbb{R}^N)\big),\\ b&\in M\big(\Omega_T\times\mathbb{R}\big)\ \text{such that }\big|b(x,t,\xi)\big|\leq \hat{b}(x,t)\ \text{a.e. for some }\hat{b}\in L^{q_1}\big((0,T);L^{q_2}(\Omega)\big) \end{align*} with $q_1,q_2>1$ such that $$\frac{2}{q_1}+\frac{N}{q_2}=2-N\kappa\quad\text{for some }\kappa\in(0,\tfrac{2}{N}).$$ Letting $w=\Phi(u)$ and assuming the inverse $\Phi^{-1}:[0,\infty)\to[0,\infty)$ to be locally H\"older continuous, this entails H\"older regularity for bounded weak solutions of $$u_t=\Delta\Phi(u)+\nabla\cdot\big(a(x,t)u\big)+b(x,t,u)$$ and, accordingly, covers a wide array of taxis type structures. In particular, many chemotaxis frameworks with nonlinear diffusion, which cannot be covered by the standard literature, fall into this category. After rigorously treating local H\"older regularity, we also extend the regularity result to the associated initial-boundary value problem for boundary conditions of flux-type., Comment: 46 pages

Chronic school absenteeism can lead to poorer academic performance and school engagement for students. It is also a risk factor for school dropout, which is associated with many long-term health impacts. This report uses data from the 2022 National Health Interview Survey (NHIS) to describe the percentage of children ages 5-17 who experienced chronic school absenteeism due to illness, injury, or disability by sociodemographic and health factors.

Fermionic gradient flow in combination with the short-flow-time expansion provides a computational method where the renormalisation of hadronic matrix elements on the lattice can be simplified to address e.g. the issue that operators with different mass dimension can mix. We demonstrate our gradient flow renormalisation procedure by determining matrix elements of four-quark operators describing neutral meson mixing or meson lifetimes. While meson mixing calculations are well-established on the lattice and serve to validate our procedure, a lattice calculation of matrix elements for heavy meson lifetimes is still outstanding. Preliminary results for mesons formed of a charm and strange quark are presented., Comment: 11 pages, 7 figures, 1 table, Contribution to the 41st International Symposium on Lattice Field Theory, July 28th - August 3rd 2024, Liverpool, UK. V2: A mistake in the analysis has been corrected leading to updated figures and final values for the DeltaQ=0 bag parameters

We report on the first measurement of $J/\psi$ photoproduction from nuclei in the photon energy range of $7$ to $10.8$ GeV, extending above and below the photoproduction threshold in the free proton of $\sim8.2$ GeV. The experiment used a tagged photon beam incident on deuterium, helium, and carbon, and the GlueX detector at Jefferson Lab to measure the semi-inclusive $A(\gamma,e^+e^-p)$ reaction with a dilepton invariant mass $M(e^+e^-)\sim m_{J/\psi}=3.1$ GeV. The incoherent $J/\psi$ photoproduction cross sections in the measured nuclei are extracted as a function of the incident photon energy, momentum transfer, and proton reconstructed missing light-cone momentum fraction. Comparisons with theoretical predictions assuming a dipole form factor allow extracting a gluonic radius for bound protons of $\sqrt{\langle r^2\rangle}=0.85\pm0.14$ fm. The data also suggest an excess of the measured cross section for sub-threshold production and for interactions with high missing light-cone momentum fraction protons. The measured enhancement can be explained by modified gluon structure for high-virtuality bound-protons.

In this paper we consider a chemotaxis system with signal consumption and degenerate diffusion of the form \begin{align*} \left\lbrace \begin{array}{r@{}l@{\quad}l} &u_t=\nabla\cdot\big(D(u)\nabla u-uS(u)\nabla v\big)+f(u,v),\\ &v_t=\Delta v- uv,\\ \end{array}\right. \end{align*} in a bounded domain $\Omega\subset\mathbb{R}^{N}$ with smooth boundary subjected to no-flux and homogeneous Neumann boundary conditions. Herein, the diffusion coefficient $D\in C^0([0,\infty))\cap C^2((0,\infty))$ is assumed to satisfy $D(0)=0$, $D(s)>0$ on $(0,\infty)$, $D'(s)\geq 0$ on $(0,\infty)$ and that there are $s_0>0$, $p>1$ and $C_D>0$ such that $$s D'(s)\leq C_D D(s)\quad\text{and}\quad C_D s^{p-1}\leq D(s)\quad\text{for }s\in[0,s_0].$$ The sensitivity function $S\in C^2([0,\infty))$ and the source term $f\in C^{1}([0,\infty)\times[0,\infty))$ are supposed to be nonnegative. We show that for all suitably regular initial data $(u_0,v_0)$ satisfying $u_0\geq \delta_0>0$ and $v_0\not\equiv 0$ there is a time-local classical solution and - despite the degeneracy at $0$ - the solution satisfies an extensibility criterion of the form $$\text{either}\quad T_{max}=\infty,\quad\text{or}\quad\limsup_{t\nearrow T_{max}}\|u(\cdot,t)\|_{L^\infty(\Omega)}=\infty.$$ Moreover, as a by-product of our analysis, we prove that a classical solution on $\Omega\times(0,T)$ obeying $\|u(\cdot,t)\|_{L^\infty(\Omega)}\leq M_u$ for all $t\in(0,T)$ and emanating from initial data $(u_0,v_0)$ as specified above remains strictly positive throughout $\Omega\times(0,T)$, i.e. one can find $\delta_u=\delta_u(T,\delta_0, M_u,\|v_0\|_{W^{1,\infty}(\Omega)})>0$ such that $$u(x,t)\geq\delta_u\quad\text{for all }(x,t)\in\Omega\times(0,T).$$ Together, the results indicate that the formation of a dead-core in these chemotaxis systems with a degenerate diffusion are impossible before the blow-up time., Comment: 7 pages

We introduce a new hair modeling method that uses a dual representation of classical hair strands and 3D Gaussians to produce accurate and realistic strand-based reconstructions from multi-view data. In contrast to recent approaches that leverage unstructured Gaussians to model human avatars, our method reconstructs the hair using 3D polylines, or strands. This fundamental difference allows the use of the resulting hairstyles out-of-the-box in modern computer graphics engines for editing, rendering, and simulation. Our 3D lifting method relies on unstructured Gaussians to generate multi-view ground truth data to supervise the fitting of hair strands. The hairstyle itself is represented in the form of the so-called strand-aligned 3D Gaussians. This representation allows us to combine strand-based hair priors, which are essential for realistic modeling of the inner structure of hairstyles, with the differentiable rendering capabilities of 3D Gaussian Splatting. Our method, named Gaussian Haircut, is evaluated on synthetic and real scenes and demonstrates state-of-the-art performance in the task of strand-based hair reconstruction.

Ultrasound is a hand-held, low-cost, non-invasive medical imaging modality which plays a vital role in diagnosing various diseases. Despite this, many rural and remote communities do not have access to ultrasound scans due to the lack of local experts trained to perform them. To address this challenge, we built a mixed reality and haptics-based tele-ultrasound system to enable an expert to precisely guide a novice remotely in carrying out an ultrasound exam. The precision and flexibility of our solution makes it more practical than existing tele-ultrasound solutions. We tested the system in Skidegate on the islands of Haida Gwaii, BC, Canada, with the experts positioned 754 km away at the University of British Columbia, Vancouver, Canada. We performed 11 scans with 10 novices and 2 experts. The experts were tasked with acquiring 5 target images and measurements in the epigastric region. The novices of various backgrounds and ages were all inexperienced in mixed reality and were not required to have prior ultrasound experience. The captured images were evaluated by two radiologists who were not present for the tests. These results are discussed along with new insights into the human computer interaction in such a system. We show that human teleoperation is feasible and can achieve high performance for completing remote ultrasound procedures, even at a large distance and with completely novice followers., Comment: 8 pages, 10 figures, submitted to IEEE VR 2025

Recovering the underlying clustering of a set $U$ of $n$ points by asking pair-wise same-cluster queries has garnered significant interest in the last decade. Given a query $S \subset U$, $|S|=2$, the oracle returns yes if the points are in the same cluster and no otherwise. For adaptive algorithms with pair-wise queries, the number of required queries is known to be $\Theta(nk)$, where $k$ is the number of clusters. However, non-adaptive schemes require $\Omega(n^2)$ queries, which matches the trivial $O(n^2)$ upper bound attained by querying every pair of points. To break the quadratic barrier for non-adaptive queries, we study a generalization of this problem to subset queries for $|S|>2$, where the oracle returns the number of clusters intersecting $S$. Allowing for subset queries of unbounded size, $O(n)$ queries is possible with an adaptive scheme (Chakrabarty-Liao, 2024). However, the realm of non-adaptive algorithms is completely unknown. In this paper, we give the first non-adaptive algorithms for clustering with subset queries. Our main result is a non-adaptive algorithm making $O(n \log k \cdot (\log k + \log\log n)^2)$ queries, which improves to $O(n \log \log n)$ when $k$ is a constant. We also consider algorithms with a restricted query size of at most $s$. In this setting we prove that $\Omega(\max(n^2/s^2,n))$ queries are necessary and obtain algorithms making $\tilde{O}(n^2k/s^2)$ queries for any $s \leq \sqrt{n}$ and $\tilde{O}(n^2/s)$ queries for any $s \leq n$. We also consider the natural special case when the clusters are balanced, obtaining non-adaptive algorithms which make $O(n \log k) + \tilde{O}(k)$ and $O(n\log^2 k)$ queries. Finally, allowing two rounds of adaptivity, we give an algorithm making $O(n \log k)$ queries in the general case and $O(n \log \log k)$ queries when the clusters are balanced.

We introduce Gaussian Garments, a novel approach for reconstructing realistic simulation-ready garment assets from multi-view videos. Our method represents garments with a combination of a 3D mesh and a Gaussian texture that encodes both the color and high-frequency surface details. This representation enables accurate registration of garment geometries to multi-view videos and helps disentangle albedo textures from lighting effects. Furthermore, we demonstrate how a pre-trained graph neural network (GNN) can be fine-tuned to replicate the real behavior of each garment. The reconstructed Gaussian Garments can be automatically combined into multi-garment outfits and animated with the fine-tuned GNN.

Generating realistic human motion is essential for many computer vision and graphics applications. The wide variety of human body shapes and sizes greatly impacts how people move. However, most existing motion models ignore these differences, relying on a standardized, average body. This leads to uniform motion across different body types, where movements don't match their physical characteristics, limiting diversity. To solve this, we introduce a new approach to develop a generative motion model based on body shape. We show that it's possible to train this model using unpaired data by applying cycle consistency, intuitive physics, and stability constraints, which capture the relationship between identity and movement. The resulting model generates diverse, physically plausible, and dynamically stable human motions that are both quantitatively and qualitatively more realistic than current state-of-the-art methods. More details are available on our project page https://CarstenEpic.github.io/humos/., Comment: Accepted in ECCV'24. Project page: https://CarstenEpic.github.io/humos/

The data that underlies automated methods in computer vision and machine learning, such as image retrieval and fine-grained recognition, often comes from crowdsourcing. In contexts that rely on the intrinsic motivation of users, we seek to understand how the application design affects a user's willingness to contribute and the quantity and quality of the data they capture. In this project, we designed three versions of a camera-based mobile crowdsourcing application, which varied in the amount of labeling effort requested of the user and conducted a user study to evaluate the trade-off between the level of user-contributed information requested and the quantity and quality of labeled images collected. The results suggest that higher levels of user labeling do not lead to reduced contribution. Users collected and annotated the most images using the application version with the highest requested level of labeling with no decrease in user satisfaction. In preliminary experiments, the additional labeled data supported increased performance on an image retrieval task.

For users navigating travel e-commerce websites, the process of researching products and making a purchase often results in intricate browsing patterns that span numerous sessions over an extended period of time. The resulting clickstream data chronicle these user journeys and present valuable opportunities to derive insights that can significantly enhance personalized recommendations. We introduce TRACE, a novel transformer-based approach tailored to generate rich user embeddings from live multi-session clickstreams for real-time recommendation applications. Prior works largely focus on single-session product sequences, whereas TRACE leverages site-wide page view sequences spanning multiple user sessions to model long-term engagement. Employing a multi-task learning framework, TRACE captures comprehensive user preferences and intents distilled into low-dimensional representations. We demonstrate TRACE's superior performance over vanilla transformer and LLM-style architectures through extensive experiments on a large-scale travel e-commerce dataset of real user journeys, where the challenges of long page-histories and sparse targets are particularly prevalent. Visualizations of the learned embeddings reveal meaningful clusters corresponding to latent user states and behaviors, highlighting TRACE's potential to enhance recommendation systems by capturing nuanced user interactions and preferences, Comment: RecSys Workshop on Recommenders in Tourism (RecTour 2024), October 14th-18th, 2024, co-located with the 18th ACM Conference on Recommender Systems, Bari, Italy

Nonlinear responses in transport experiments may unveil information and generate new phenomena in materials that are not accessible at linear order due to symmetry constraints. While the linear anomalous Hall response strictly requires the absence of time-reversal symmetry, the second-order, thus nonlinear, Hall response needs broken inversion symmetry. Recently, much effort has been made to obtain a second-order Hall voltage in response to a longitudinal ac driving current, both to obtain information about band geometric quantities and for its useful technological applications, including rectification and frequency doubling. Typically, additional material engineering is required in noncentrosymmetric systems to obtain second-order responses since it obeys a stringent crystallographic symmetry constraint. To circumvent this, an alternative route is to apply a dc electric field. In this Letter, we uncover an electric field induced second-order anomalous Hall effect in an inversion-broken system possessing unconventional Rashba bands. We establish that the quantum metric, a geometrical feature of electronic wave functions providing information on non-trivial structure of Bloch bands, is responsible for providing the nonlinear Hall response., Comment: 7 pages, 5 figures

In today's rapidly evolving military landscape, advancing artificial intelligence (AI) in support of wargaming becomes essential. Despite reinforcement learning (RL) showing promise for developing intelligent agents, conventional RL faces limitations in handling the complexity inherent in combat simulations. This dissertation proposes a comprehensive approach, including targeted observation abstractions, multi-model integration, a hybrid AI framework, and an overarching hierarchical reinforcement learning (HRL) framework. Our localized observation abstraction using piecewise linear spatial decay simplifies the RL problem, enhancing computational efficiency and demonstrating superior efficacy over traditional global observation methods. Our multi-model framework combines various AI methodologies, optimizing performance while still enabling the use of diverse, specialized individual behavior models. Our hybrid AI framework synergizes RL with scripted agents, leveraging RL for high-level decisions and scripted agents for lower-level tasks, enhancing adaptability, reliability, and performance. Our HRL architecture and training framework decomposes complex problems into manageable subproblems, aligning with military decision-making structures. Although initial tests did not show improved performance, insights were gained to improve future iterations. This study underscores AI's potential to revolutionize wargaming, emphasizing the need for continued research in this domain.

In the domain of combat simulations, the training and deployment of deep reinforcement learning (RL) agents still face substantial challenges due to the dynamic and intricate nature of such environments. Unfortunately, as the complexity of the scenarios and available information increases, the training time required to achieve a certain threshold of performance does not just increase, but often does so exponentially. This relationship underscores the profound impact of complexity in training RL agents. This paper introduces a novel approach that addresses this limitation in training artificial intelligence (AI) agents using RL. Traditional RL methods have been shown to struggle in these high-dimensional, dynamic environments due to real-world computational constraints and the known sample inefficiency challenges of RL. To overcome these limitations, we propose a method of localized observation abstraction using piecewise linear spatial decay. This technique simplifies the state space, reducing computational demands while still preserving essential information, thereby enhancing AI training efficiency in dynamic environments where spatial relationships are often critical. Our analysis reveals that this localized observation approach consistently outperforms the more traditional global observation approach across increasing scenario complexity levels. This paper advances the research on observation abstractions for RL, illustrating how localized observation with piecewise linear spatial decay can provide an effective solution to large state representation challenges in dynamic environments.

Integrating Electronic Health Records (EHR) and the application of machine learning present opportunities for enhancing the accuracy and accessibility of data-driven diabetes prediction. In particular, developing data-driven machine learning models can provide early identification of patients with high risk for diabetes, potentially leading to more effective therapeutic strategies and reduced healthcare costs. However, regulation restrictions create barriers to developing centralized predictive models. This paper addresses the challenges by introducing a federated learning approach, which amalgamates predictive models without centralized data storage and processing, thus avoiding privacy issues. This marks the first application of federated learning to predict diabetes using real clinical datasets in Canada extracted from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) without crossprovince patient data sharing. We address class-imbalance issues through downsampling techniques and compare federated learning performance against province-based and centralized models. Experimental results show that the federated MLP model presents a similar or higher performance compared to the model trained with the centralized approach. However, the federated logistic regression model showed inferior performance compared to its centralized peer., Comment: 10 pages

The jet cross-section and jet-substructure observables in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV were measured by the PHENIX Collaboration at the Relativistic Heavy Ion Collider (RHIC). Jets are reconstructed from charged-particle tracks and electromagnetic-calorimeter clusters using the anti-$k_{t}$ algorithm with a jet radius $R=0.3$ for jets with transverse momentum within $8.0

Periodically driven systems offer a perfect breeding ground for out-of-equilibrium engineering of topological boundary states at zero energy ($0$-mode), as well as finite energy ($\pi$-mode), with the latter having no static analog. The Floquet operator and the effective Floquet Hamiltonian, which encapsulate the stroboscopic features of the driven system, capture both spectral and localization properties of the $0$- and $\pi$-modes but sometimes fail to provide complete topological characterization, especially when $0$- and $\pi$-modes coexist. In this work, we utilize the spectral localizer, a powerful local probe that can provide numerically efficient, spatially local, and energy-resolved topological characterization. In particular, we apply the spectral localizer to the effective Floquet Hamiltonian for driven one- and two-dimensional topological systems with no or limited symmetries and are able to assign topological invariants, or local markers, that characterize the $0$- and the $\pi$-boundary modes individually and unambiguously. Due to the spatial resolution, we also demonstrate that the extracted topological invariants are suitable for studying driven disordered systems and can even capture disorder-induced phase transitions., Comment: 11 pages, 6 figures; Comments are welcome

Against the backdrop of enthusiasm for large language models (LLMs), there is an urgent need to scientifically assess their capabilities and shortcomings. This is nontrivial in part because it is difficult to find tasks which the models have not encountered during training. Utilizing symbolic graphics programs, we propose a domain well-suited to test multiple spatial-semantic reasoning skills of LLMs. Popular in computer graphics, these programs procedurally generate visual data. While LLMs exhibit impressive skills in general program synthesis and analysis, symbolic graphics programs offer a new layer of evaluation: they allow us to test an LLM's ability to answer different-grained semantic-level questions of the images or 3D geometries without a vision encoder. To semantically understand the symbolic programs, LLMs would need to possess the ability to "imagine" and reason how the corresponding graphics content would look with only the symbolic description. We use this task to evaluate LLMs by creating a large benchmark for the semantic visual understanding of symbolic graphics programs, built procedurally with minimal human effort. Particular emphasis is placed on transformations of images that leave the image level semantics invariant while introducing significant changes to the underlying program. We evaluate commercial and open-source LLMs on our benchmark to assess their ability to reason about visual output of programs, finding that LLMs considered stronger at reasoning generally perform better. Lastly, we introduce a novel method to improve this ability -- Symbolic Instruction Tuning (SIT), in which the LLM is finetuned with pre-collected instruction data on symbolic graphics programs. Interestingly, we find that SIT not only improves LLM's understanding on symbolic programs, but it also improves general reasoning ability on various other benchmarks., Comment: Technical Report v2 (46 pages, 24 figures, project page: https://sgp-bench.github.io/, substantial update from v1)

We investigate intrinsic superconducting diode effect in the unconventional superconducting state of $d$-wave altermagnets. We find large diode efficiencies in the wide-reaching finite-momentum pairing regimes of the phase diagram. Remarkably, even perfect diode efficiency of 100% can be obtained in the presence of an external magnetic field. We attribute the largest efficiencies to competition between multiple zero-momentum (BCS) and finite-momentum superconducting states, which is connected to a topological nodal-to-nodeless transition in altermagnets with magnetic field, making our results applicable to a wide range of altermagnets., Comment: 6 pages, 3 figures

Surface sensing is widely employed in health diagnostics, manufacturing and safety monitoring. Advances in mobile sensing affords this potential for context awareness in mobile computing, typically with a single sensing modality. Emerging multimodal large-scale language models offer new opportunities. We propose MultiSurf-GPT, which utilizes the advanced capabilities of GPT-4o to process and interpret diverse modalities (radar, microscope and multispectral data) uniformly based on prompting strategies (zero-shot and few-shot prompting). We preliminarily validated our framework by using MultiSurf-GPT to identify low-level information, and to infer high-level context-aware analytics, demonstrating the capability of augmenting context-aware insights. This framework shows promise as a tool to expedite the development of more complex context-aware applications in the future, providing a faster, more cost-effective, and integrated solution., Comment: 6 pages. MOBILEHCI Adjunct '24, 26th International Conference on Mobile Human-Computer Interaction, September 30-October 3, 2024, Melbourne, VIC, Australia

The gas-phase abundance of carbon, x_C = C/H, and its depletion factors are essential parameters for understanding the gas and solid compositions that are ultimately incorporated into planets. The majority of protoplanetary disks are born in clusters and, as a result, are exposed to external FUV radiation. These FUV photons potentially affect the disk's evolution, chemical composition, and line excitation. We present the first detection of the [CI]609um fine-structure line of neutral carbon (CI), achieved with ALMA, toward one of these disks, d203-506, in the Orion Nebula Cluster. We also report the detection of CI forbidden and permitted lines (from electronically excited states up to 10 eV) observed with JWST in the IR. These lines trace the irradiated outer disk and photo-evaporative wind. Contrary to the common belief that these IR lines are C+ recombination lines, we find that they are dominated by FUV-pumping of CI followed by fluorescence cascades. They trace the transition from atomic to molecular gas, and their intensities scale with G0. The lack of outstanding IR OI fluorescent emission, however, implies a sharper attenuation of external FUV radiation with E > 12 eV (~Lyman-beta). This is related to a lower effective FUV dust absorption cross section compared to that of interstellar grains, implying a more prominent role for FUV shielding by the CI photoionization continuum. The [CI]609um intensity is proportional to N(CI) and can be used to infer x_C. We derive x_C ~ 1.4E-4. This implies that there is no major depletion of volatile carbon compared to x_C measured in the natal cloud, hinting at a young disk. We also show that external FUV radiation impacts the outer disk and wind by vertically shifting the water freeze-out depth, which results in less efficient grain growth and settling. This shift leads to nearly solar gas-phase C/O abundance ratios in these irradiated layers., Comment: Accepted for publication in A&A Letters. 14 pages including Appendices

Superconductivity in the recently discovered altermagnetic materials hosts large prospects for both fundamental physics and technological applications. In this work we show that a characteristic spin-sublattice locking in altermagnets puts severe constraints on possible superconducting pairing. In particular, we uncover that the most common form of superconductivity, uniform $s$-wave spin-singlet pairing is not possible to achieve in altermagnets. Considering an effective model for a $d_{x^2-y^2}$-wave altermagnet on a square lattice, we instead find that the most likely forms of spin-singlet pairing have $d_{x^2-y^2}$- or extended $s$-wave symmetry. We also find that the simplest form of equal-spin-triplet $p$-wave pairing is not allowed, but it can only exist as a mixed-spin-triplet $p$-wave state. We verify these constraints on pairing within an interaction-induced model of altermagnetism, where we also establish their validity for finite-momentum pairing. Additionally we discuss the possible pairing symmetries for odd-frequency superconducting pairing. Due to the generality of our results, they are applicable to both intrinsic superconductivity and proximity-induced superconductivity in altermagnet-superconductor hybrid junctions., Comment: 10 pages, 3 figures, 2 tables

The focus of this paper is on 3D motion editing. Given a 3D human motion and a textual description of the desired modification, our goal is to generate an edited motion as described by the text. The key challenges include the scarcity of training data and the need to design a model that accurately edits the source motion. In this paper, we address both challenges. We propose a methodology to semi-automatically collect a dataset of triplets comprising (i) a source motion, (ii) a target motion, and (iii) an edit text, introducing the new MotionFix dataset. Access to this data allows us to train a conditional diffusion model, TMED, that takes both the source motion and the edit text as input. We develop several baselines to evaluate our model, comparing it against models trained solely on text-motion pair datasets, and demonstrate the superior performance of our model trained on triplets. We also introduce new retrieval-based metrics for motion editing, establishing a benchmark on the evaluation set of MotionFix. Our results are promising, paving the way for further research in fine-grained motion generation. Code, models, and data are available at https://motionfix.is.tue.mpg.de/ ., Comment: SIGGRAPH Asia 2024 Camera Ready, Project page: https://motionfix.is.tue.mpg.de

Electrodes for decoding speech from electromyography (EMG) are typically placed on the face, requiring adhesives that are inconvenient and skin-irritating if used regularly. We explore a different device form factor, where dry electrodes are placed around the neck instead. 11-word, multi-speaker voiced EMG classifiers trained on data recorded with this device achieve 92.7% accuracy. Ablation studies reveal the importance of having more than two electrodes on the neck, and phonological analyses reveal similar classification confusions between neck-only and neck-and-face form factors. Finally, speech-EMG correlation experiments demonstrate a linear relationship between many EMG spectrogram frequency bins and self-supervised speech representation dimensions.

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions (Beltagy et al., 2020a) and group-query attention (Ainslie et al., 2023). We also train the 2B and 9B models with knowledge distillation (Hinton et al., 2015) instead of next token prediction. The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2-3 times bigger. We release all our models to the community.

Background: Low vitamin D intake and high prevalence of vitamin D deficiency (serum 25-hydroxyvitamin D concentration < 50 nmol/L) among Aboriginal and Torres Strait Islander peoples highlight a need for public health strategies to improve vitamin D status. As few foods contain naturally occurring vitamin D, fortification strategies may be needed to improve vitamin D intake and status among Aboriginal and Torres Strait Islander peoples. Objective: We aimed to model vitamin D food fortification scenarios among Aboriginal and Torres Strait Islander peoples. Methods: We used nationally representative food consumption data (n=4,109) and vitamin D food composition data to model four food fortification scenarios. The modelling for Scenario 1 included foods and maximum vitamin D concentrations permitted for fortification in Australia: i) dairy products and alternatives, ii) butter/margarine/oil spreads, iii) formulated beverages, and iv) selected ready-to-eat breakfast cereals. The modelling for Scenarios 2a-c included some vitamin D concentrations higher than permitted in Australia; Scenario 2c included bread, which is not permitted for vitamin D fortification in Australia. Scenario 2a: i) dairy products and alternatives, ii) butter/margarine/oil spreads, iii) formulated beverages. Scenario 2b: as per Scenario 2a plus selected ready-to-eat breakfast cereals. Scenario 2c: as per Scenario 2b plus bread. Results: Vitamin D fortification of a range of staple foods could potentially increase vitamin D intake among Aboriginal and Torres Strait Islander peoples by ~ 3-6 {\mu}g/day. Scenario 2c showed the highest potential median vitamin D intake increase to ~ 8 {\mu}g/day. Across all modelled scenarios, none of the participants had vitamin D intake above the Australian upper level of intake of 80 {\mu}g/day.

Electrochemical Impedance Spectroscopy (EIS) is a crucial technique for assessing corrosion of a metallic materials. The analysis of EIS hinges on the selection of an appropriate equivalent circuit model (ECM) that accurately characterizes the system under study. In this work, we systematically examined the applicability of three commonly used ECMs across several typical material degradation scenarios. By applying Bayesian Inference to simulated corrosion EIS data, we assessed the suitability of these ECMs under different corrosion conditions and identified regions where the EIS data lacks sufficient information to statistically substantiate the ECM structure. Additionally, we posit that the traditional approach to EIS analysis, which often requires measurements to very low frequencies, might not be always necessary to correctly model the appropriate ECM. Our study assesses the impact of omitting data from low to medium-frequency ranges on inference results and reveals that a significant portion of low-frequency measurements can be excluded without substantially compromising the accuracy of extracting system parameters. Further, we propose simple checks to the posterior distributions of the ECM components and posterior predictions, which can be used to quantitatively evaluate the suitability of a particular ECM and the minimum frequency required to be measured. This framework points to a pathway for expediting EIS acquisition by intelligently reducing low-frequency data collection and permitting on-the-fly EIS measurements