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The study compared the relationship of the contract plan of student evaluation and a traditional type of evaluation, for changes in knowledge and attitudes during a college course in ecological relationships. The Syracuse Environmental Awareness Test was used to measure both cognitive and affective changes. In the experimental group the contract plan of evaluation was utilized. Subjects were evaluated on the number of course objectives which were completed satisfactorily. In the control group, a traditional type of student evaluation was used. The students were evaluated on the scores they received on three examinations. Although there was no statistically significant difference between the two groups when compared, there were several factors which gave credence to the hypothesis that the traditional method was superior to the contract method. First, there was a significant increase in cognitive knowledge for the control group between the pretest and the posttest, while in the experimental group there was no significant increase. Second, the experimental group had a larger percentage of Health Science majors than did the control group. If it is valid to assume that Health Science majors would be more highly motivated to do well in a course in their major field of study; then the experimental group should have demonstrated greater increases in cognitive and affective behavior. (RC)

Novel view synthesis techniques predominantly utilize RGB cameras, inheriting their limitations such as the need for sufficient lighting, susceptibility to motion blur, and restricted dynamic range. In contrast, event cameras are significantly more resilient to these limitations but have been less explored in this domain, particularly in large-scale settings. Current methodologies primarily focus on front-facing or object-oriented (360-degree view) scenarios. For the first time, we introduce 3D Gaussians for event-based novel view synthesis. Our method reconstructs large and unbounded scenes with high visual quality. We contribute the first real and synthetic event datasets tailored for this setting. Our method demonstrates superior novel view synthesis and consistently outperforms the baseline EventNeRF by a margin of 11-25% in PSNR (dB) while being orders of magnitude faster in reconstruction and rendering., Comment: 15 pages, 10 figures and 3 tables; project page: https://4dqv.mpi-inf.mpg.de/E3DGS/; International Conference on 3D Vision (3DV) 2025

Consider a diffusion process X, solution of a time-homogeneous stochastic differential equation. We assume that the diffusion process X is observed at discrete times, at high frequency, which means that the time step tends toward zero. In addition, the drift and diffusion coefficients of the process X are assumed to be unknown. In this paper, we study the minimax rates of convergence of the nonparametric estimators of the square of the diffusion coefficient. Two observation schemes are considered depending on the estimation interval. The square of the diffusion coefficient is estimated on the real line from repeated observations of the process X, where the number of diffusion paths tends to infinity. For the case of a compact estimation interval, we study the nonparametric estimation of the square of the diffusion coefficient constructed from a single diffusion path on one side and from repeated observations on the other side, where the number of trajectories tends to infinity. In each of these cases, we establish minimax convergence rates of the risk of estimation of the diffusion coefficient over a space of Holder functions., Comment: 32 pages

In the context of binary classification of trajectories generated by time-homogeneous stochastic differential equations, we consider a mixture model of two diffusion processes characterized by a stochastic differential equation whose drift coefficient depends on the class or label, which is modeled as a discrete random variable taking two possible values, and whose diffusion coefficient is independent of the class. We assume that the drift and diffusion coefficients are unknown as well as the law of the discrete random variable that models the class. In this paper, we study the minimax convergence rate of the resulting nonparametric plug-in classifier under different sets of assumptions on the mixture model considered. As the plug-in classifier is based on nonparametric estimators of the coefficients of the mixture model, we also provide a minimax convergence rate of the risk of estimation of the drift coefficients on the real line., Comment: 36 pages

A lattice point in $\mathbb{R}^2$ is a point $(x,y)$ with $x,y\in\mathbb{Z}$, and a lattice triangle is a triangle whose three vertices are all lattice points. We investigate the integers $k$ with the property that if $T$ is a lattice triangle with $3$ boundary points and $k$ points in the interior, then all $k$ boundary points must be collinear., Comment: 10 pages, 0 figures

We present a numerical and analytical study of the so-called `toron' solution of the stationary axisymmetric Einstein equations in vacuum expressed in terms of elliptic functions. The asymptotic behavior of this solution coincides with the one of the NUT solution, i.e., it has a `gravimagnetic' mass known as the NUT parameter while the ordinary mass vanishes. The physical properties of this spacetime are studied via ray tracing. The results are compared to known geodesic flows in Schwarzschild, Kerr and NUT spacetimes to discuss similarities and differences, with a particular emphasis on the comparison of NUT and toron spacetimes.

Maintaining a "green" mainline branch, where all builds pass successfully, is crucial but challenging in fast-paced, large-scale software development environments, particularly with concurrent code changes in large monorepos. SubmitQueue, a system designed to address these challenges, speculatively executes builds and only lands changes with successful outcomes. However, despite its effectiveness, the system faces inefficiencies in resource utilization, leading to a high rate of premature build aborts and delays in landing smaller changes blocked by larger conflicting ones. This paper introduces enhancements to SubmitQueue, focusing on optimizing resource usage and improving build prioritization. Central to this is our innovative probabilistic model, which distinguishes between changes with shorter and longer build times to prioritize builds for more efficient scheduling. By leveraging a machine learning model to predict build times and incorporating this into the probabilistic framework, we expedite the landing of smaller changes blocked by conflicting larger time-consuming changes. Additionally, introducing a concept of speculation threshold ensures that only the most likely builds are executed, reducing unnecessary resource consumption. After implementing these enhancements across Uber's major monorepos (Go, iOS, and Android), we observed a reduction in Continuous Integration (CI) resource usage by approximately 53%, CPU usage by 44%, and P95 waiting times by 37%. These improvements highlight the enhanced efficiency of SubmitQueue in managing large-scale software changes while maintaining a green mainline.

In this paper we show that the chain recurrent set of a flow of automorphisms on a connected Lie group coincides with the central subgroup of the flow, if the group is decomposable. Moreover, in the decomposable case, the flow satisfies the restriction property. Furthermore, the restriction of any flow of automorphisms to the connected component of the identity of its central subgroup is chain transitive.

This paper presents a decentralized, online planning approach for scalable maneuver planning for large constellations. While decentralized, rule-based strategies have facilitated efficient scaling, optimal decision-making algorithms for satellite maneuvers remain underexplored. As commercial satellite constellations grow, there are benefits of online maneuver planning, such as using real-time trajectory predictions to improve state knowledge, thereby reducing maneuver frequency and conserving fuel. We address this gap in the research by treating the satellite maneuver planning problem as a Markov decision process (MDP). This approach enables the generation of optimal maneuver policies online with low computational cost. This formulation is applied to the low Earth orbit collision avoidance problem, considering the problem of an active spacecraft deciding to maneuver to avoid a non-maneuverable object. We test the policies we generate in a simulated low Earth orbit environment, and compare the results to traditional rule-based collision avoidance techniques., Comment: 9 Pages, 5 Figures, 5 Tables, to be published in IEEE Aeroconf 2025

We introduce Gradient Agreement Filtering (GAF) to improve on gradient averaging in distributed deep learning optimization. Traditional distributed data-parallel stochastic gradient descent involves averaging gradients of microbatches to calculate a macrobatch gradient that is then used to update model parameters. We find that gradients across microbatches are often orthogonal or negatively correlated, especially in late stages of training, which leads to memorization of the training set, reducing generalization. In this paper, we introduce a simple, computationally effective way to reduce gradient variance by computing the cosine distance between micro-gradients during training and filtering out conflicting updates prior to averaging. We improve validation accuracy with significantly smaller microbatch sizes. We also show this reduces memorizing noisy labels. We demonstrate the effectiveness of this technique on standard image classification benchmarks including CIFAR-100 and CIFAR-100N-Fine. We show this technique consistently outperforms validation accuracy, in some cases by up to 18.2\% compared to traditional training approaches while reducing the computation required nearly an order of magnitude because we can now rely on smaller microbatch sizes without destabilizing training.

We make the case that variational algorithm ansatzes for near-term quantum computing are well-suited for the quantum circuit cutting strategy. Previous demonstrations of circuit cutting focused on the exponential execution and postprocessing costs due to the cuts needed to partition a circuit topology, leading to overly pessimistic evaluations of the approach. This work observes that the ansatz Clifford structure and variational parameter pruning significantly reduce these costs. By keeping track of the limited set of correct subcircuit initializations and measurements, we reduce the number of experiments needed by up to 16x, matching and beating the error mitigation offered by classical shadows tomography. By performing reconstruction as a sparse tensor contraction, we scale the feasible ansatzes to over 200 qubits with six ansatz layers, beyond the capability of prior work.

We study the $k=3$ Read-Rezayi quantum Hall state by means of a purely bosonic matrix product state formulation, which is described in detail. We calculate the density profiles in the presence of bulk quasi-holes of six different types: one for each $\mathbb{Z}_3$ parafermion sector. From the density profiles, we calculate the (local) spins of these quasi-holes. By employing a spin-statistics relation, we obtain the exchange statistics parameters. Our results, which are entirely based on local properties of the quasi-holes, corroborate previous results obtained by explicitly braiding quasi-holes, showing that the exchange statistics can be read off from the monodromy properties of the wave functions, i.e., that the associated Berry phase vanishes. We also discuss the entanglement spectrum, to show that our bosonic matrix product state formulation correctly captures the $\mathbb{Z}_3$ parafermionic structure of the $k=3$ Read-Rezayi states., Comment: 25 pages, 8 figures. See the accompanying paper arXiv:2412.14879. v3: added references

We define a measurable spin for the edge of a lowest Landau level and incompressible fractional quantum Hall state in the presence of an Abelian or non-Abelian bulk quasiparticle. We show that this quantity takes a fractional value inherited from the fractional spin of the bulk quasiparticle. We present a geometric picture that does not rely on global symmetries of the wavefunction but is able to treat quasiparticles and edges with different shapes. We study finite-size many-body wavefunctions on the cylinder with circular quasiparticles and straight edges. Our results are supported by matrix-product-state calculations for the Laughlin and the k=3 Read-Rezayi states., Comment: 4.5 + 2 pages. 2 figures. See also the accompanying paper: Fagerlund and Ardonne, arXiv:2412.14889 (2024). v3: references added

We introduce SLayR, Scene Layout Generation with Rectified flow. State-of-the-art text-to-image models achieve impressive results. However, they generate images end-to-end, exposing no fine-grained control over the process. SLayR presents a novel transformer-based rectified flow model for layout generation over a token space that can be decoded into bounding boxes and corresponding labels, which can then be transformed into images using existing models. We show that established metrics for generated images are inconclusive for evaluating their underlying scene layout, and introduce a new benchmark suite, including a carefully designed repeatable human-evaluation procedure that assesses the plausibility and variety of generated layouts. In contrast to previous works, which perform well in either high variety or plausibility, we show that our approach performs well on both of these axes at the same time. It is also at least 5x times smaller in the number of parameters and 37% faster than the baselines. Our complete text-to-image pipeline demonstrates the added benefits of an interpretable and editable intermediate representation., Comment: 34 pages, 29 figures, 5 tables

A new family of groups, called trickle groups, is presented. These groups generalize right-angled Artin and Coxeter groups, as well as cactus groups. A trickle group is defined by a presentation with relations of the form $xy = zx$ and $x^\mu = 1$, that are governed by a simplicial graph, called a trickle graph, endowed with a partial ordering on the vertices, a vertex labeling, and an automorphism of the star of each vertex. We show several examples of trickle groups, including extended cactus groups, certain finite-index subgroups of virtual cactus groups, Thompson group F, and ordered quandle groups. A terminating and confluent rewriting system is established for trickle groups, enabling the definition of normal forms and a solution to the word problem. An alternative solution to the word problem is also presented, offering a simpler formulation akin to Tits' approach for Coxeter groups and Green's for graph products of cyclic groups. A natural notion of a parabolic subgraph of a trickle graph is introduced. The subgroup generated by the vertices of such a subgraph is called a standard parabolic subgroup and it is shown to be the trickle group associated with the subgraph itself. The intersection of two standard parabolic subgroups is also proven to be a standard parabolic subgroup. If only relations of the form $xy = zx$ are retained in the definition of a trickle group, then the resulting group is called a preGarside trickle group. Such a group is proved to be a preGarside group, a torsion-free group, and a Garside group if and only if its associated trickle graph is finite and complete.

A semidomain is a subsemiring of an integral domain. We call a semidomain $S$ additively reduced if $0$ is the only invertible element of the monoid $(S, +)$, while we say that $S$ is additively Furstenberg if every non-invertible element of $(S,+)$ can be expressed as the sum of an atom and an element of $S$. In this paper, we study a variant of the Goldbach conjecture within the framework of group semidomains $S[G]$ and group series semidomains $S[\![G]\!]$, where $S$ is both an additively reduced and additively Furstenberg semidomain and $G$ is a torsion-free abelian group. In particular, we show that every non-constant polynomial expression in $S[G]$ can be written as the sum of at most two irreducibles if and only if the condition $\mathscr{A}_+(S) = S^\times$ holds., Comment: 14 pages

We study the non-hermitian Kitaev chain model, for arbitrary complex parameters. In particular, we give a concise characterisation of the curves of eigenvalues in the complex plane in the infinite size limit. Using this solution, we characterise under which conditions the skin effect is absent, and for which eigenstates this is the case. We also fully determine the region in parameter space for which the model has a zero mode., Comment: 18 pages, 7 figures

Nuclear magnetic resonance instruments are becoming available to the do-it-yourself community. The challenges encountered in the endeavor to build a magnetic resonance imaging instrument from scratch were confronted in a four-day hackathon at Singapore University of Technology and Design in spring 2024. One day was devoted to educational lectures and three days to system construction and testing. Seventy young researchers from all parts of the world formed six teams focusing on magnet, gradient coil, RF coil, console, system integration, and design, which together produced a working MRI instrument in three days. The different steps, encountered challenges, and their solutions are reported., Comment: 49 pages, 23 figures, comments welcome (this paper is meant to be useful to people constructing their own MRI systems)

Monitored Natural Attenuation (MNA) is gaining prominence as an effective method for managing soil and groundwater contamination due to its cost-efficiency and minimal environmental disruption. Despite its benefits, MNA necessitates extensive groundwater monitoring to ensure that contaminant levels decrease to meet safety standards. This study expands the capabilities of PyLEnM, a Python package designed for long-term environmental monitoring, by incorporating new algorithms to enhance its predictive and analytical functionalities. We introduce methods to estimate the timeframe required for contaminants like Sr-90 and I-129 to reach regulatory safety standards using linear regression and to forecast future contaminant levels with the Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Additionally, Random Forest regression is employed to identify factors influencing the time to reach safety standards. Our methods are illustrated using data from the Savannah River Site (SRS) F-Area, where preliminary findings reveal a notable downward trend in contaminant levels, with variability linked to initial concentrations and groundwater flow dynamics. The Bi-LSTM model effectively predicts contaminant concentrations for the next four years, demonstrating the potential of advanced time series analysis to improve MNA strategies and reduce reliance on manual groundwater sampling. The code, along with its usage instructions, validation, and requirements, is available at: https://github.com/csplevuanh/pylenm_extension., Comment: The paper will be withdrawn to fix some work issues with the sections on Bi-LSTM models

The use of non-invasive sensors & systems to measure particle beam characteristics is a crucial part of modern accelerator control systems due to their ability to return real time passive measurements without impacting the beam quality. Simulations, which can predict these sensors' behaviour and performance under anticipated accelerator conditions, are valuable tools to ensure confidence in the sensors' functionality prior to a physical bench test. This paper details the design, testing, and results of two sensor models developed using CST Studio Suite software. One model is an elliptical, large-aperture beam position monitor (BPM) for which vertical & horizontal position signal linearity was analyzed. The second model is an AC current transformer (ACCT) beam current monitor (BCM), which was used to search for potential fault cases within the BCM and beam pipe flange gaps. Fermilab Proton Improvement Plan II (PIP-II) accelerator beam conditions were applied, and special focus is given to the discovery of linearity variations within the BPM as well as the use of frequency domain techniques in the BCM fault case analyses., Comment: 5 pages, 9 figures. To be submitted to the Journal of Instrumentation

This paper is about the epistemology of quantum theory. We establish a new result about a limitation to knowledge of its central object -- the quantum state of the universe. We show that, if the universal quantum state can be assumed to be a typical unit vector from a high-dimensional subspace of Hilbert space (such as the subspace defined by a low-entropy macro-state as prescribed by the Past Hypothesis), then no observation can determine (or even just narrow down significantly) which vector it is. Typical state vectors, in other words, are observationally indistinguishable from each other. Our argument is based on a typicality theorem from quantum statistical mechanics. We also discuss how theoretical considerations that go beyond the empirical evidence might bear on this fact and on our knowledge of the universal quantum state., Comment: 20 pages LaTeX, no figures

Purpose: To develop an MRI technique for free-breathing 3D whole-liver quantification of water T1, water T2, proton density fat fraction (PDFF), R2*. Methods: An Eight-echo spoiled gradient echo pulse sequence with spiral readout was developed by interleaving inversion recovery and T2 magnetization preparation. We propose a neural network based on a 4D and a 3D implicit neural representation (INR) which simultaneously learns the motion deformation fields and the static reference frame MRI subspace images respectively. Water and fat singular images were separated during network training, with no need of performing retrospective water-fat separation. T1, T2, R2* and proton density fat fraction (PDFF) produced by the proposed method were validated in vivo on 10 healthy subjects, using quantitative maps generated from conventional scans as reference. Results: Our results showed minimal bias and narrow 95% limits of agreement on T1, T2, R2* and PDFF values in the liver compared to conventional breath-holding scans. Conclusions: INR-MRF enabled co-registered 3D whole liver T1, T2, R2* and PDFF mapping in a single free-breathing scan.

Mechanical objects have been widely used at low temperatures for decades, for various applications; from quantum fluids sensing with vibrating wires or tuning forks, to torsional oscillators for the study of mechanical properties of glasses, and finally micro and nano-mechanical objects with the advent of clean room technologies. These small structures opened up new possibilities to experimentalists, thanks to their small size. We report on the characterization of purely metallic goalpost nano-mechanical structures, which are employed today for both quantum fluids studies (especially quantum turbulence in $^4$He, $^3$He) and intrinsic friction studies (Two-Level-Systems unraveling). Extending existing literature, we demonstrate the analytic modeling of the resonances, in good agreement with numerical simulations, for both first and second mechanical modes. Especially, the impact of the curvature of the whole structure (and therefore, in-built surface stress) is analyzed, together with nonlinear properties. We demonstrate that these are of geometrical origin, and device-dependent. Motion and forces are expressed in meters and Newtons experienced at the level of the goalpost's paddle, for any magnitude or curvature, which is of particular importance for quantum fluids and solids studies.

This paper presents a solution to the problem of optimal ground station selection for low-Earth orbiting (LEO) space missions that enables mission operators to precisely design their ground segment performance and costs. Space mission operators are increasingly turning to Ground-Station-as-a-Service (GSaaS) providers to supply the terrestrial communications segment to reduce costs and increase network size. However, this approach leads to a new challenge of selecting the optimal service providers and station locations for a given mission. We consider the problem of ground station selection as an optimization problem and present a general solution framework that allows mission designers to set their overall optimization objective and constrain key mission performance variables such as total data downlink, total mission cost, recurring operational cost, and maximum communications time-gap. We solve the problem using integer programming (IP). To address computational scaling challenges, we introduce a surrogate optimization approach where the optimal station selection is determined based on solving the problem over a reduced time domain. Two different IP formulations are evaluated using randomized selections of LEO satellites of varying constellation sizes. We consider the networks of the commercial GSaaS providers Atlas Space Operations, Amazon Web Services (AWS) Ground Station, Azure Orbital Ground Station, Kongsberg Satellite Services (KSAT), Leaf Space, and Viasat Real-Time Earth. We compare our results against standard operational practices of integrating with one or two primary ground station providers., Comment: 13 pages, 3 tables, 4 figures, submitted to IEEE Aeroconf 2025

In this article, the Hausdorff dimension is estimated using the box-counting dimension and the information dimension. It is shown that the former is an upper bound for the Hausdorff dimension, while the latter is a lower bound for the box-counting dimension, and that the information dimension is an upper bound for the Hausdorff dimension. Additionally, the dimension of the Henon attractor is estimated using the box-counting dimension., Comment: 14 pages, in Spanish language

In this work, we will study a numerical method that allows finding an approximation of the exact solution for a in-situ combustion model using the nonlinear mixed complementary method, which is a variation of the Newtons method for solving nonlinear systems based on an implicit finite difference scheme and a nonlinear algorithm mixed complementarity, FDA-MNCP. The method has the advantage of provide a global convergence in relation to the finite difference method and method of Newton that only has local convergence. The theory is applied to model in-situ combustion, which can be rewritten in the form of mixed complementarity also we do a comparison with the FDA-NCP method, Comment: 17 Pages

Achieving high accuracy with computational efficiency in brain disease classification from Magnetic Resonance Imaging (MRI) scans is challenging, particularly when both coarse and fine-grained distinctions are crucial. Current deep learning methods often struggle to balance accuracy with computational demands. We propose Lite-FBCN, a novel Lightweight Fast Bilinear Convolutional Network designed to address this issue. Unlike traditional dual-network bilinear models, Lite-FBCN utilizes a single-network architecture, significantly reducing computational load. Lite-FBCN leverages lightweight, pre-trained CNNs fine-tuned to extract relevant features and incorporates a channel reducer layer before bilinear pooling, minimizing feature map dimensionality and resulting in a compact bilinear vector. Extensive evaluations on cross-validation and hold-out data demonstrate that Lite-FBCN not only surpasses baseline CNNs but also outperforms existing bilinear models. Lite-FBCN with MobileNetV1 attains 98.10% accuracy in cross-validation and 69.37% on hold-out data (a 3% improvement over the baseline). UMAP visualizations further confirm its effectiveness in distinguishing closely related brain disease classes. Moreover, its optimal trade-off between performance and computational efficiency positions Lite-FBCN as a promising solution for enhancing diagnostic capabilities in resource-constrained and or real-time clinical environments.

We introduce Spurfies, a novel method for sparse-view surface reconstruction that disentangles appearance and geometry information to utilize local geometry priors trained on synthetic data. Recent research heavily focuses on 3D reconstruction using dense multi-view setups, typically requiring hundreds of images. However, these methods often struggle with few-view scenarios. Existing sparse-view reconstruction techniques often rely on multi-view stereo networks that need to learn joint priors for geometry and appearance from a large amount of data. In contrast, we introduce a neural point representation that disentangles geometry and appearance to train a local geometry prior using a subset of the synthetic ShapeNet dataset only. During inference, we utilize this surface prior as additional constraint for surface and appearance reconstruction from sparse input views via differentiable volume rendering, restricting the space of possible solutions. We validate the effectiveness of our method on the DTU dataset and demonstrate that it outperforms previous state of the art by 35% in surface quality while achieving competitive novel view synthesis quality. Moreover, in contrast to previous works, our method can be applied to larger, unbounded scenes, such as Mip-NeRF 360., Comment: https://geometric-rl.mpi-inf.mpg.de/spurfies/

Rapid development in quantum computing leads to the appearance of several quantum applications. Quantum Fourier Transformation (QFT) sits at the heart of many of these applications. Existing work leverages SAT solver or heuristics to generate a hardware-compliant circuit for QFT by inserting SWAP gates to remap logical qubits to physical qubits. However, they might face problems such as long compilation time due to the huge search space for SAT solver or suboptimal outcome in terms of the number of cycles to finish all gate operations. In this paper, we propose a domain-specific hardware mapping approach for QFT. We unify our insight of relaxed ordering and unit exploration in QFT to search for a qubit mapping solution with the help of program synthesis tools. Our method is the first one that guarantees linear-depth QFT circuits for Google Sycamore, IBM heavy-hex, and the lattice surgery, with respect to the number of qubits. Compared with state-of-the-art approaches, our method can save up to 53% in SWAP gate and 92% in depth., Comment: arXiv admin note: text overlap with arXiv:2312.16114

Anonymity is an important principle online. However, malicious actors have long used misleading identities to conduct fraud, spread disinformation, and carry out other deceptive schemes. With the advent of increasingly capable AI, bad actors can amplify the potential scale and effectiveness of their operations, intensifying the challenge of balancing anonymity and trustworthiness online. In this paper, we analyze the value of a new tool to address this challenge: "personhood credentials" (PHCs), digital credentials that empower users to demonstrate that they are real people -- not AIs -- to online services, without disclosing any personal information. Such credentials can be issued by a range of trusted institutions -- governments or otherwise. A PHC system, according to our definition, could be local or global, and does not need to be biometrics-based. Two trends in AI contribute to the urgency of the challenge: AI's increasing indistinguishability from people online (i.e., lifelike content and avatars, agentic activity), and AI's increasing scalability (i.e., cost-effectiveness, accessibility). Drawing on a long history of research into anonymous credentials and "proof-of-personhood" systems, personhood credentials give people a way to signal their trustworthiness on online platforms, and offer service providers new tools for reducing misuse by bad actors. In contrast, existing countermeasures to automated deception -- such as CAPTCHAs -- are inadequate against sophisticated AI, while stringent identity verification solutions are insufficiently private for many use-cases. After surveying the benefits of personhood credentials, we also examine deployment risks and design challenges. We conclude with actionable next steps for policymakers, technologists, and standards bodies to consider in consultation with the public., Comment: 63 pages, 7 figures, 5 tables; minor additions to acknowledgments and wording changes for clarity; corrected typo; updated email address reference for author

First time in six decades, uncountable infinite exact solutions of FitzHugh-Nagumo model with diffusion have been found. FitzHugh-Nagumo model is a nonlinear dynamical system applicable to neurosciences, chemical kinetics, cell division, population dynamics, electronics, epidemiology, cardiac physiology and pattern formation. Non-classical symmetry analysis has been carried out to find invariant solutions. In many ways this finding makes the corpus of asymptotics and numerics obsolete. Non singular, physically stable and meaningful solutions could now be found without distorting the actual model or introducing forced conditions.

Conventional approaches for the automated red-teaming of large language models (LLMs) aim to identify prompts that elicit toxic outputs from a frozen language model (the defender). This often results in the prompting model (the adversary) producing text that is unlikely to arise during autoregression. In response, we propose a reinforcement learning formulation of LLM red-teaming designed to discover prompts that both (1) elicit toxic outputs from a defender and (2) have low perplexity as scored by that defender. These prompts are the most pertinent in a red-teaming setting because the defender generates them with high probability. We solve this formulation with an online and weakly supervised form of Identity Preference Optimization (IPO), attacking models ranging from 137M to 7.8B parameters. Our policy performs competitively, producing prompts that induce defender toxicity at a rate of 2-23 times higher than baseline across model scales. Importantly, these prompts have lower perplexity than both automatically generated and human-written attacks. Furthermore, our method creates black-box attacks with 5.4-14 times increased toxicity. To assess the downstream utility of our method, we use rollouts from our policy as negative examples for downstream toxicity tuning and demonstrate improved safety., Comment: 10 pages, 7 pages of appendix, 3 tables, 3 figures

Different from human nature, it is still common practice today for vision tasks to train deep learning models only initially and on fixed datasets. A variety of approaches have recently addressed handling continual data streams. However, extending these methods to manage out-of-distribution (OOD) scenarios has not effectively been investigated. On the other hand, it has recently been shown that non-continual neural mesh models exhibit strong performance in generalizing to such OOD scenarios. To leverage this decisive property in a continual learning setting, we propose incremental neural mesh models that can be extended with new meshes over time. In addition, we present a latent space initialization strategy that enables us to allocate feature space for future unseen classes in advance and a positional regularization term that forces the features of the different classes to consistently stay in respective latent space regions. We demonstrate the effectiveness of our method through extensive experiments on the Pascal3D and ObjectNet3D datasets and show that our approach outperforms the baselines for classification by $2-6\%$ in the in-domain and by $6-50\%$ in the OOD setting. Our work also presents the first incremental learning approach for pose estimation. Our code and model can be found at https://github.com/Fischer-Tom/iNeMo., Comment: ECCV-24

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTSPEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.

We continue the study of the retraction from an Artin group to a standard parabolic subgroup introduced by Blufstein, Charney, Paris and the second author. Using right and left retractions we obtain new results on minimal parabolic subgroups, intersection of parabolic subgroups, double cosets with respect to parabolic subgroups and conjugacy classes in Artin groups.

Category-level 3D pose estimation is a fundamentally important problem in computer vision and robotics, e.g. for embodied agents or to train 3D generative models. However, so far methods that estimate the category-level object pose require either large amounts of human annotations, CAD models or input from RGB-D sensors. In contrast, we tackle the problem of learning to estimate the category-level 3D pose only from casually taken object-centric videos without human supervision. We propose a two-step pipeline: First, we introduce a multi-view alignment procedure that determines canonical camera poses across videos with a novel and robust cyclic distance formulation for geometric and appearance matching using reconstructed coarse meshes and DINOv2 features. In a second step, the canonical poses and reconstructed meshes enable us to train a model for 3D pose estimation from a single image. In particular, our model learns to estimate dense correspondences between images and a prototypical 3D template by predicting, for each pixel in a 2D image, a feature vector of the corresponding vertex in the template mesh. We demonstrate that our method outperforms all baselines at the unsupervised alignment of object-centric videos by a large margin and provides faithful and robust predictions in-the-wild. Our code and data is available at https://github.com/GenIntel/uns-obj-pose3d.

In this study, we present a novel platform based on scanning microwave microscopy for manipulating and detecting tiny vibrations of nanoelectromechanical resonators using a single metallic tip. The tip is placed on the top of a grounded silicon nitride membrane, acting as a movable top gate of the coupled resonator. We demonstrate its ability to map mechanical modes and investigate mechanical damping effects in a capacitive coupling scheme, based on its spatial resolution. We also manipulate the energy transfer coherently between the mode of the scanning tip and the underlying silicon nitride membrane, via parametric coupling. Typical features of optomechanics, such as anti-damping and electromechanically induced transparency, have been observed. Since the microwave optomechanical technology is fully compatible with quantum electronics and very low temperature conditions, it should provide a powerful tool for studying phonon tunnelling between two spatially separated vibrating elements, which could potentially be applied to quantum sensing.

After initial tests performed during previous observing runs, a Newtonian Calibrator (NCal) system was developed and installed on the Virgo gravitational wave detector for the O4 observing run. This system, which is continuously operated, provides the absolute calibration of Virgo for this run. Its $1\mbox{-}\sigma$ uncertainty of $0.17\%$ on the amplitudes of the injected signals is better than that obtained with other calibration techniques like the photon calibrator (PCal). This paper presents this NCal system and details the different sources of uncertainties, Comment: Class. Quantum Grav (2024)

This data curation work introduces the first large-scale dataset of radial k-space and DICOM data for breast DCE-MRI acquired in diagnostic breast MRI exams. Our dataset includes case-level labels indicating patient age, menopause status, lesion status (negative, benign, and malignant), and lesion type for each case. The public availability of this dataset and accompanying reconstruction code will support research and development of fast and quantitative breast image reconstruction and machine learning methods.