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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.

A non-uniform implicit-explicit L1 mixed finite element method (IMEX-L1-MFEM) is investigated for a class of time-fractional partial integro-differential equations (PIDEs) with space-time dependent coefficients and non-self-adjoint elliptic part. The proposed fully discrete method combines an IMEX-L1 method on a graded mesh in the temporal variable with a mixed finite element method in spatial variables. The focus of the study is to analyze stability results and to establish optimal error estimates, up to a logarithmic factor, for both the solution and the flux in $L^2$-norm when the initial data $u_0\in H_0^1(\Omega)\cap H^2(\Omega)$. Additionally, an error estimate in $L^\infty$-norm is derived for 2D problems. All the derived estimates and bounds in this article remain valid as $\alpha\to 1^{-}$, where $\alpha$ is the order of the Caputo fractional derivative. Finally, the results of several numerical experiments conducted at the end of this paper are confirming our theoretical findings.

Many motivated extensions of the standard model include new light bosons, such as axions and dark photons, which can mix with the ordinary photon. This latter, when in a dilute plasma, can be dressed by an effective plasma mass. If this is equal to the mass of the new degree of freedom, then a resonance takes place and the probability of transition between states is enhanced. This phenomenon of resonance conversion is at the very basis of multiple probes of dark matter, and it is typically studied within the so-called Landau-Zener approximation for level crossings. This latter is known to break down in a variety of scenarios, such as multiple level crossings or when the de Broglie wave length of the new boson is comparable to the scale over which the background plasma varies. We develop a flexible code adopting a 3+1 formalism in flat spacetime to perform non-linear simulations of systems with photons and new ultralight bosons in the presence of plasma. Our code currently allows to evolve one-dimensional systems, which are the ones of interest for this first study, but can be easily extended to treat three-dimensional spaces, and can be adapted to describe a plethora of realistic astrophysical and cosmological situations. Here we use it to study the breakdown of the Landau-Zener approximation in the case of multiple level crossings and when the slowly-varying plasma approximation ceases to be valid. In this first paper we detail our code and use it to study non-turbulent plasma, where small scale fluctuations can be neglected; their treatment will be considered in an upcoming publication., Comment: 16 pages, 13 figures

The gravitational physics landscape is evolving rapidly, driven by our ability to study strong-field regions, in particular black holes. Black Holes Inside and Out gathered world experts to discuss the status of the field and prospects ahead. We hope that the ideas and perspectives are a source of inspiration. Structure: Black Hole Evaporation - 50 Years by William Unruh The Stability Problem for Extremal Black Holes by Mihalis Dafermos The Entropy of Black Holes by Robert M. Wald The Non-linear Regime of Gravity by Luis Lehner Black Holes Galore in D > 4 by Roberto Emparan Same as Ever: Looking for (In)variants in the Black Holes Landscape by Carlos A. R. Herdeiro Black Holes, Cauchy Horizons, and Mass Inflation by Matt Visser The Backreaction Problem for Black Holes in Semiclassical Gravity by Adrian del Rio Black Holes Beyond General Relativity by Enrico Barausse and Jutta Kunz Black Holes as Laboratories: Searching for Ultralight Fields by Richard Brito Primordial Black Holes from Inflation by Misao Sasaki Tests of General Relativity with Future Detectors by Emanuele Berti Black Holes as Laboratories: Tests of General Relativity by Ruth Gregory and Samaya Nissanke Simulating Black Hole Imposters by Frans Pretorius Black Hole Spectroscopy: Status Report by Gregorio Carullo VLBI as a Precision Strong Gravity Instrument by Paul Tiede Testing the nature of compact objects and the black hole paradigm by Mariafelicia De Laurentis and Paolo Pani Some Thoughts about Black Holes in Asymptotic Safety by Alessia Platania Black Hole Evaporation in Loop Quantum Gravity by Abhay Ashtekar How the Black Hole Puzzles are Resolved in String Theory by Samir D. Mathur Quantum Black Holes: From Regularization to Information Paradoxes by Niayesh Afshordi and Stefano Liberati, Comment: 221 pages, 21 contributions

The advancement of dense visual simultaneous localization and mapping (SLAM) has been greatly facilitated by the emergence of neural implicit representations. Neural implicit encoding SLAM, a typical example of which is NICE-SLAM, has recently demonstrated promising results in large-scale indoor scenes. However, these methods typically rely on temporally dense RGB-D image streams as input in order to function properly. When the input source does not support high frame rates or the camera movement is too fast, these methods often experience crashes or significant degradation in tracking and mapping accuracy. In this paper, we propose EvenNICER-SLAM, a novel approach that addresses this issue through the incorporation of event cameras. Event cameras are bio-inspired cameras that respond to intensity changes instead of absolute brightness. Specifically, we integrated an event loss backpropagation stream into the NICE-SLAM pipeline to enhance camera tracking with insufficient RGB-D input. We found through quantitative evaluation that EvenNICER-SLAM, with an inclusion of higher-frequency event image input, significantly outperforms NICE-SLAM with reduced RGB-D input frequency. Our results suggest the potential for event cameras to improve the robustness of dense SLAM systems against fast camera motion in real-world scenarios.

Recent progress in large language models and access to large-scale robotic datasets has sparked a paradigm shift in robotics models transforming them into generalists able to adapt to various tasks, scenes, and robot modalities. A large step for the community are open Vision Language Action models which showcase strong performance in a wide variety of tasks. In this work, we study the visual generalization capabilities of three existing robotic foundation models, and propose a corresponding evaluation framework. Our study shows that the existing models do not exhibit robustness to visual out-of-domain scenarios. This is potentially caused by limited variations in the training data and/or catastrophic forgetting, leading to domain limitations in the vision foundation models. We further explore OpenVLA, which uses two pre-trained vision foundation models and is, therefore, expected to generalize to out-of-domain experiments. However, we showcase catastrophic forgetting by DINO-v2 in OpenVLA through its failure to fulfill the task of depth regression. To overcome the aforementioned issue of visual catastrophic forgetting, we propose a gradual backbone reversal approach founded on model merging. This enables OpenVLA which requires the adaptation of the visual backbones during initial training -- to regain its visual generalization ability. Regaining this capability enables our ReVLA model to improve over OpenVLA by a factor of 77% and 66% for grasping and lifting in visual OOD tasks .

Current methods to learn controllers for autonomous vehicles (AVs) focus on behavioural cloning. Being trained only on exact historic data, the resulting agents often generalize poorly to novel scenarios. Simulators provide the opportunity to go beyond offline datasets, but they are still treated as complicated black boxes, only used to update the global simulation state. As a result, these RL algorithms are slow, sample-inefficient, and prior-agnostic. In this work, we leverage a differentiable simulator and design an analytic policy gradients (APG) approach to training AV controllers on the large-scale Waymo Open Motion Dataset. Our proposed framework brings the differentiable simulator into an end-to-end training loop, where gradients of the environment dynamics serve as a useful prior to help the agent learn a more grounded policy. We combine this setup with a recurrent architecture that can efficiently propagate temporal information across long simulated trajectories. This APG method allows us to learn robust, accurate, and fast policies, while only requiring widely-available expert trajectories, instead of scarce expert actions. We compare to behavioural cloning and find significant improvements in performance and robustness to noise in the dynamics, as well as overall more intuitive human-like handling.

CLIP is a powerful and widely used tool for understanding images in the context of natural language descriptions to perform nuanced tasks. However, it does not offer application-specific fine-grained and structured understanding, due to its generic nature. In this work, we aim to adapt CLIP for fine-grained and structured -- in the form of tabular data -- visual understanding of museum exhibits. To facilitate such understanding we (a) collect, curate, and benchmark a dataset of 200K+ image-table pairs, and (b) develop a method that allows predicting tabular outputs for input images. Our dataset is the first of its kind in the public domain. At the same time, the proposed method is novel in leveraging CLIP's powerful representations for fine-grained and tabular understanding. The proposed method (MUZE) learns to map CLIP's image embeddings to the tabular structure by means of a proposed transformer-based parsing network (parseNet). More specifically, parseNet enables prediction of missing attribute values while integrating context from known attribute-value pairs for an input image. We show that this leads to significant improvement in accuracy. Through exhaustive experiments, we show the effectiveness of the proposed method on fine-grained and structured understanding of museum exhibits, by achieving encouraging results in a newly established benchmark. Our dataset and source-code can be found at: https://github.com/insait-institute/MUZE, Comment: Accepted to ECCV 2024

Generalist vision models aim for one and the same architecture for a variety of vision tasks. While such shared architecture may seem attractive, generalist models tend to be outperformed by their bespoken counterparts, especially in the case of panoptic segmentation. We address this problem by introducing two key contributions, without compromising the desirable properties of generalist models. These contributions are: (i) a positional-embedding (PE) based loss for improved centroid regressions; (ii) Edge Distance Sampling (EDS) for the better separation of instance boundaries. The PE-based loss facilitates a better per-pixel regression of the associated instance's centroid, whereas EDS contributes by carefully handling the void regions (caused by missing labels) and smaller instances. These two simple yet effective modifications significantly improve established baselines, while achieving state-of-the-art results among all generalist solutions. More specifically, our method achieves a panoptic quality(PQ) of 52.5 on the COCO dataset, which is an improvement of 10 points over the best model with similar approach (Painter), and is superior by 2 to the best performing diffusion-based method Pix2Seq-$\mathcal{D}$. Furthermore, we provide insights into and an in-depth analysis of our contributions through exhaustive experiments. Our source code and model weights will be made publicly available.

Detecting a compact subsolar object would have profound implications in physics, the reach of which depends on the nature of the object. Here we explore such consequences for a putative subsolar-mass gravitational wave event detected by the LIGO-Virgo-KAGRA Collaboration. We forecast that the nature of a subsolar binary (made of light neutron stars, primordial black holes, or more exotic compact objects) can be inferred with a great statistical confidence level already during the ongoing fourth observing run, based on the large tidal deformability effects on the signal. The detection of a primordial black hole would have implications for cosmology and dark matter scenarios, while the measurement of the tidal deformability of a subsolar neutron star could rule out or confirm the existence of strange stars made of quarks., Comment: 5+4 pages, 3 figures

The tidal Love numbers of self-gravitating compact objects describe their response to external tidal perturbations, such as those from a companion in a binary system, offering valuable insights into their internal structure. For static tidal fields, asymptotically flat black holes in vacuum exhibit vanishing Love numbers in general relativity, even though this property is sensitive to the presence of an external environment. In this work we study the tidal deformability of black holes surrounded by thin accretion disks, showing that the Love numbers could be large enough to mask any effect of modified gravity and to intrinsically limit tidal tests of black-hole mimickers. Furthermore, we investigate the measurability of the tidal parameters with next-generation gravitational wave experiments, like LISA and Einstein Telescope. Our findings suggest that these parameters could be measured with high precision, providing a powerful tool to probe the environment around coalescing binary systems., Comment: 13 pages, 4 figures

3D Gaussian Splatting (3DGS) has become the de facto method of 3D representation in many vision tasks. This calls for the 3D understanding directly in this representation space. To facilitate the research in this direction, we first build a large-scale dataset of 3DGS using the commonly used ShapeNet and ModelNet datasets. Our dataset ShapeSplat consists of 65K objects from 87 unique categories, whose labels are in accordance with the respective datasets. The creation of this dataset utilized the compute equivalent of 2 GPU years on a TITAN XP GPU. We utilize our dataset for unsupervised pretraining and supervised finetuning for classification and segmentation tasks. To this end, we introduce \textbf{\textit{Gaussian-MAE}}, which highlights the unique benefits of representation learning from Gaussian parameters. Through exhaustive experiments, we provide several valuable insights. In particular, we show that (1) the distribution of the optimized GS centroids significantly differs from the uniformly sampled point cloud (used for initialization) counterpart; (2) this change in distribution results in degradation in classification but improvement in segmentation tasks when using only the centroids; (3) to leverage additional Gaussian parameters, we propose Gaussian feature grouping in a normalized feature space, along with splats pooling layer, offering a tailored solution to effectively group and embed similar Gaussians, which leads to notable improvement in finetuning tasks.

Object detection, particularly open-vocabulary object detection, plays a crucial role in Earth sciences, such as environmental monitoring, natural disaster assessment, and land-use planning. However, existing open-vocabulary detectors, primarily trained on natural-world images, struggle to generalize to remote sensing images due to a significant data domain gap. Thus, this paper aims to advance the development of open-vocabulary object detection in remote sensing community. To achieve this, we first reformulate the task as Locate Anything on Earth (LAE) with the goal of detecting any novel concepts on Earth. We then developed the LAE-Label Engine which collects, auto-annotates, and unifies up to 10 remote sensing datasets creating the LAE-1M - the first large-scale remote sensing object detection dataset with broad category coverage. Using the LAE-1M, we further propose and train the novel LAE-DINO Model, the first open-vocabulary foundation object detector for the LAE task, featuring Dynamic Vocabulary Construction (DVC) and Visual-Guided Text Prompt Learning (VisGT) modules. DVC dynamically constructs vocabulary for each training batch, while VisGT maps visual features to semantic space, enhancing text features. We comprehensively conduct experiments on established remote sensing benchmark DIOR, DOTAv2.0, as well as our newly introduced 80-class LAE-80C benchmark. Results demonstrate the advantages of the LAE-1M dataset and the effectiveness of the LAE-DINO method., Comment: 10 pages, 5 figures

In any extension of General Relativity (GR), extra fundamental degrees of freedom couple to gravity. Besides deforming GR forecasts in a theory-dependent way, this coupling generically introduces extra modes in the gravitational-wave signal. We propose a novel theory-agnostic test of gravity to search for these nongravitational modes in black hole merger ringdown signals. To leading order in the GR deviations, their frequencies and damping times match those of a test scalar or vector field in a Kerr background, with only amplitudes and phases as free parameters. This test will be highly valuable for future detectors, which will achieve signal-to-noise ratios higher than 100 (and as high as 1000 for space-based detectors such as LISA). Such sensitivity will allow measurement of these modes with amplitude ratios as low as 0.05 for ground-based detectors (and as low as 0.008 for LISA), relative to the fundamental mode, enabling stringent agnostic constraints or detection of scalar/vector modes. By applying this test to GW150914, GW190521, and GW200129, we find that the current evidence for an extra mode is comparable to that for the first gravitational overtone, but its inclusion modifies the inferred remnant spin., Comment: 10 pages, 9 figures. v2: minor modifications, new explicit example, and new results on precessing binaries. Submitted version

The spectrum of the quasinormal modes of the gravitational waves emitted during the ringdown phase following the merger of two black holes is of primary importance in gravitational astronomy. However, the spectrum is extremely sensitive to small disturbances of the system, thus potentially jeopardizing the predictions of the gravitational wave observables. We offer an analytical and intuitive explanation of such an instability and its properties based on the transfer matrix approach of quantum mechanics. We also give a simple interpretation of the fact that the prompt ringdown response in the time domain and the black hole greybody factor receive parametrically small corrections, thus being robust observables., Comment: 5 pages, 2 figures, supplementary material available

With the emergence of mobile devices, there is a growing demand for an efficient model to restore any degraded image for better perceptual quality. However, existing models often require specific learning modules tailored for each degradation, resulting in complex architectures and high computation costs. Different from previous work, in this paper, we propose a unified manner to achieve joint embedding by leveraging the inherent similarities across various degradations for efficient and comprehensive restoration. Specifically, we first dig into the sub-latent space of each input to analyze the key components and reweight their contributions in a gated manner. The intrinsic awareness is further integrated with contextualized attention in an X-shaped scheme, maximizing local-global intertwining. Extensive comparison on benchmarking all-in-one restoration setting validates our efficiency and effectiveness, i.e., our network sets new SOTA records while reducing model complexity by approximately -82% in trainable parameters and -85\% in FLOPs. Our code will be made publicly available at:https://github.com/Amazingren/AnyIR., Comment: Efficient Any Image Restoration

The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their "muon shot". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider., Comment: This document summarises the International Muon Collider Collaboration (IMCC) progress and status of the Muon Collider R&D programme

Personalized 3D avatars require an animatable representation of digital humans. Doing so instantly from monocular videos offers scalability to broad class of users and wide-scale applications. In this paper, we present a fast, simple, yet effective method for creating animatable 3D digital humans from monocular videos. Our method utilizes the efficiency of Gaussian splatting to model both 3D geometry and appearance. However, we observed that naively optimizing Gaussian splats results in inaccurate geometry, thereby leading to poor animations. This work achieves and illustrates the need of accurate 3D mesh-type modelling of the human body for animatable digitization through Gaussian splats. This is achieved by developing a novel pipeline that benefits from three key aspects: (a) implicit modelling of surface's displacements and the color's spherical harmonics; (b) binding of 3D Gaussians to the respective triangular faces of the body template; (c) a novel technique to render normals followed by their auxiliary supervision. Our exhaustive experiments on three different benchmark datasets demonstrates the state-of-the-art results of our method, in limited time settings. In fact, our method is faster by an order of magnitude (in terms of training time) than its closest competitor. At the same time, we achieve superior rendering and 3D reconstruction performance under the change of poses., Comment: 15 pages, eccv, 2024

Contrastive learning (CL) for Vision Transformers (ViTs) in image domains has achieved performance comparable to CL for traditional convolutional backbones. However, in 3D point cloud pretraining with ViTs, masked autoencoder (MAE) modeling remains dominant. This raises the question: Can we take the best of both worlds? To answer this question, we first empirically validate that integrating MAE-based point cloud pre-training with the standard contrastive learning paradigm, even with meticulous design, can lead to a decrease in performance. To address this limitation, we reintroduce CL into the MAE-based point cloud pre-training paradigm by leveraging the inherent contrastive properties of MAE. Specifically, rather than relying on extensive data augmentation as commonly used in the image domain, we randomly mask the input tokens twice to generate contrastive input pairs. Subsequently, a weight-sharing encoder and two identically structured decoders are utilized to perform masked token reconstruction. Additionally, we propose that for an input token masked by both masks simultaneously, the reconstructed features should be as similar as possible. This naturally establishes an explicit contrastive constraint within the generative MAE-based pre-training paradigm, resulting in our proposed method, Point-CMAE. Consequently, Point-CMAE effectively enhances the representation quality and transfer performance compared to its MAE counterpart. Experimental evaluations across various downstream applications, including classification, part segmentation, and few-shot learning, demonstrate the efficacy of our framework in surpassing state-of-the-art techniques under standard ViTs and single-modal settings. The source code and trained models are available at: https://github.com/Amazingren/Point-CMAE., Comment: Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning

We study the linear response of four dimensional magnetized black holes and regular topological stars arising from dimensional compactification of Einstein-Maxwell theory in five dimensions. We consider both radial and nonradial perturbations and study the stability of these solutions, both in the frequency and in the time domain. Due to the presence of magnetic fluxes in the background, axial (i.e., odd-parity) gravitational perturbations are coupled to polar (i.e., even-parity) electromagnetic perturbations (Type-I sector) whereas polar gravitational and scalar perturbations are coupled to axial electromagnetic ones (Type-II sector). We provide a comprehensive analytical and numerical study of the radial perturbations and of the Type I sector, finding no evidence of linear instabilities (besides the already known Gregory-Laflamme instability of black strings occurring only in a certain range of the parameters), even despite the fact that the effective potential for radial perturbations of topological stars is negative and divergent near the inner boundary. Ultracompact topological stars exhibit long-lived trapped modes that give rise to echoes in the time-domain response. Their prompt response is very similar to that of the corresponding black hole with comparable charge-to-mass ratio. This provides a concrete realization of ultracompact objects arising from a well-defined theory. The numerical analysis of the Type-II sector will appear in a companion paper., Comment: 23 pages, 11 figures. Comments and clarifications added, to be published in PRD

Neural implicit functions have demonstrated significant importance in various areas such as computer vision, graphics. Their advantages include the ability to represent complex shapes and scenes with high fidelity, smooth interpolation capabilities, and continuous representations. Despite these benefits, the development and analysis of implicit functions have been limited by the lack of comprehensive datasets and the substantial computational resources required for their implementation and evaluation. To address these challenges, we introduce "Implicit-Zoo": a large-scale dataset requiring thousands of GPU training days designed to facilitate research and development in this field. Our dataset includes diverse 2D and 3D scenes, such as CIFAR-10, ImageNet-1K, and Cityscapes for 2D image tasks, and the OmniObject3D dataset for 3D vision tasks. We ensure high quality through strict checks, refining or filtering out low-quality data. Using Implicit-Zoo, we showcase two immediate benefits as it enables to: (1) learn token locations for transformer models; (2) directly regress 3D cameras poses of 2D images with respect to NeRF models. This in turn leads to an improved performance in all three task of image classification, semantic segmentation, and 3D pose regression, thereby unlocking new avenues for research.

Conventional relays face challenges for transmission lines connected to inverter-based resources (IBRs). In this article, a single-ended intelligent protection of the transmission line in the zone between the grid and the PV farm is suggested. The method employs a fuzzy logic and random forest (RF)-based hybrid system to detect faults based on combined linear trend attributes of the 3-phase currents. The fault location is determined and the faulty phase is detected. RF feature selection is used to obtain the optimal linear trend feature. The performance of the methodology is examined for abnormal events such as faults, capacitor and load-switching operations simulated in PSCAD/EMTDC on IEEE 9-bus system obtained by varying various fault and switching parameters. Additionally, when validating the suggested strategy, consideration is given to the effects of conditions such as the presence of double circuit lines, PV capacity, sampling rate, data window length, noise, high impedance faults, CT saturation, compensation devices, evolving and cross-country faults, and far-end and near-end faults. The findings indicate that the suggested strategy can be used to deal with a variety of system configurations and situations while still safeguarding such complex power transmission networks., Comment: 27 pages, 20 figures

The quasinormal mode spectrum of black holes plays a crucial role in the modelling of post-merger ringdown signals. However, the spectrum is extremely sensitive to small deformations of the system and describes the linear response only in a certain (not precisely defined) timeframe after the merger. We argue here that the greybody factors, recently shown to describe the ringdown spectral amplitude at relatively high frequencies, are instead stable under small perturbations of the system and free of certain ambiguities that plague the quasinormal mode spectrum. Our analysis also unveils a nontrivial interplay: while certain ringdown quantities are dominated by the contribution of spectrally unstable quasinormal modes, these modes conspire to produce stable observables. Thus, we propose a complementary approach to ringdown studies, which circumvents some limitations of the standard quasinormal mode description., Comment: 13 pages, 11 figures, accepted for publication in PRD Letters

With the emergence of a single large model capable of successfully solving a multitude of tasks in NLP, there has been growing research interest in achieving similar goals in computer vision. On the one hand, most of these generic models, referred to as generalist vision models, aim at producing unified outputs serving different tasks. On the other hand, some existing models aim to combine different input types (aka data modalities), which are then processed by a single large model. Yet, this step of combination remains specialized, which falls short of serving the initial ambition. In this paper, we showcase that such specialization (during unification) is unnecessary, in the context of RGB-X video object tracking. Our single model tracker, termed XTrack, can remain blind to any modality X during inference time. Our tracker employs a mixture of modal experts comprising those dedicated to shared commonality and others capable of flexibly performing reasoning conditioned on input modality. Such a design ensures the unification of input modalities towards a common latent space, without weakening the modality-specific information representation. With this idea, our training process is extremely simple, integrating multi-label classification loss with a routing function, thereby effectively aligning and unifying all modalities together, even from only paired data. Thus, during inference, we can adopt any modality without relying on the inductive bias of the modal prior and achieve generalist performance. Without any bells and whistles, our generalist and blind tracker can achieve competitive performance compared to well-established modal-specific models on 5 benchmarks across 3 auxiliary modalities, covering commonly used depth, thermal, and event data.

Reconstructing missing details from degraded low-quality inputs poses a significant challenge. Recent progress in image restoration has demonstrated the efficacy of learning large models capable of addressing various degradations simultaneously. Nonetheless, these approaches introduce considerable computational overhead and complex learning paradigms, limiting their practical utility. In response, we propose \textit{DaAIR}, an efficient All-in-One image restorer employing a Degradation-aware Learner (DaLe) in the low-rank regime to collaboratively mine shared aspects and subtle nuances across diverse degradations, generating a degradation-aware embedding. By dynamically allocating model capacity to input degradations, we realize an efficient restorer integrating holistic and specific learning within a unified model. Furthermore, DaAIR introduces a cost-efficient parameter update mechanism that enhances degradation awareness while maintaining computational efficiency. Extensive comparisons across five image degradations demonstrate that our DaAIR outperforms both state-of-the-art All-in-One models and degradation-specific counterparts, affirming our efficacy and practicality. The source will be publicly made available at https://eduardzamfir.github.io/daair/

Modern neuroscience has evolved into a frontier field that draws on numerous disciplines, resulting in the flourishing of novel conceptual frames primarily inspired by physics and complex systems science. Contributing in this direction, we recently introduced a mathematical framework to describe the spatiotemporal interactions of systems of neurons using lattice field theory, the reference paradigm for theoretical particle physics. In this note, we provide a concise summary of the basics of the theory, aiming to be intuitive to the interdisciplinary neuroscience community. We contextualize our methods, illustrating how to readily connect the parameters of our formulation to experimental variables using well-known renormalization procedures. This synopsis yields the key concepts needed to describe neural networks using lattice physics. Such classes of methods are attention-worthy in an era of blistering improvements in numerical computations, as they can facilitate relating the observation of neural activity to generative models underpinned by physical principles., Comment: 16 pages, 2 figures

General relativity (GR) has proven to be a highly successful theory of gravity since its inception. The theory has thrivingly passed numerous experimental tests, predominantly in weak gravity, low relative speeds, and linear regimes, but also in the strong-field and very low-speed regimes with binary pulsars. Observable gravitational waves (GWs) originate from regions of spacetime where gravity is extremely strong, making them a unique tool for testing GR, in previously inaccessible regions of large curvature, relativistic speeds, and strong gravity. Since their first detection, GWs have been extensively used to test GR, but no deviations have been found so far. Given GR's tremendous success in explaining current astronomical observations and laboratory experiments, accepting any deviation from it requires a very high level of statistical confidence and consistency of the deviation across GW sources. In this paper, we compile a comprehensive list of potential causes that can lead to a false identification of a GR violation in standard tests of GR on data from current and future ground-based GW detectors. These causes include detector noise, signal overlaps, gaps in the data, detector calibration, source model inaccuracy, missing physics in the source and in the underlying environment model, source misidentification, and mismodeling of the astrophysical population. We also provide a rough estimate of when each of these causes will become important for tests of GR for different detector sensitivities. We argue that each of these causes should be thoroughly investigated, quantified, and ruled out before claiming a GR violation in GW observations., Comment: Review article; 1 figure; 1 table; comments welcome

With the STAR experiment at the BNL Relativisic Heavy Ion Collider, we characterize $\sqrt{s_\mathrm{NN}}$ = 200 GeV p+Au collisions by event activity (EA) measured within the pseudorapidity range $eta$ $in$ [-5, -3.4] in the Au-going direction and report correlations between this EA and hard- and soft- scale particle production at midrapidity ($\eta$ $\in$ [-1, 1]). At the soft scale, charged particle production in low-EA p+Au collisions is comparable to that in p+p collisions and increases monotonically with increasing EA. At the hard scale, we report measurements of high transverse momentum (pT) jets in events of different EAs. In contrast with the soft particle production, high-pT particle production and EA are found to be inversely related. To investigate whether this is a signal of jet quenching in high-EA events, we also report ratios of pT imbalance and azimuthal separation of dijets in high- and low-EA events. Within our measurement precision, no significant differences are observed, disfavoring the presence of jet quenching in the highest 30% EA p+Au collisions at $\sqrt{s_\mathrm{NN}}$ = 200 GeV., Comment: 12 page, 9 figures

Fermion soliton stars are a consistent model of exotic compact objects which involve a nonlinear interaction between a real scalar field and fermions through a Yukawa term. This interaction results in an effective fermion mass that depends upon the vacuum structure in the scalar potential. In this work we investigate the tidal deformations of fermion soliton stars and compute the corresponding tidal Love numbers for different model parameters. Furthermore, we discuss the existence of approximate universal relations for the electric and magnetic tidal deformabilities of these stars, and compare them with other solutions of general relativity, such as neutron stars or boson stars. These relations for fermion soliton stars are less universal than for neutron stars, but they are sufficiently different from the ordinary neutron star case that a measurement of the electric and magnetic tidal Love numbers (as potentially achievable by next-generation gravitational wave detectors) can be used to disentangle these families of compact objects. Finally, we discuss the conditions for tidal disruption of fermion soliton stars in a binary system and estimate the detectability of the electromagnetic signal associated with such tidal disruption events., Comment: 15 pages, 4 figures. v2: new figure added, matches version accepted in PRD

Tidal heating in a binary black hole system is driven by the absorption of energy and angular momentum by the black hole's horizon. Previous works have shown that this phenomenon becomes particularly significant during the late stages of an extreme mass ratio inspiral (EMRI) into a rapidly spinning massive black hole, a key focus for future low-frequency gravitational-wave observations by (for instance) the LISA mission. Past analyses have largely focused on quasi-circular inspiral geometry, with some of the most detailed studies looking at equatorial cases. Though useful for illustrating the physical principles, this limit is not very realistic astrophysically, since the population of EMRI events is expected to arise from compact objects scattered onto relativistic orbits in galactic centers through many-body events. In this work, we extend those results by studying the importance of tidal heating in equatorial EMRIs with generic eccentricities. Our results suggest that accurate modeling of tidal heating is crucial to prevent significant dephasing and systematic errors in EMRI parameter estimation. We examine a phenomenological model for EMRIs around exotic compact objects by parameterizing deviations from the black hole picture in terms of the fraction of radiation absorbed compared to the BH case. Based on a mismatch calculation we find that reflectivities as small as $|\mathcal{R}|^2 \sim \mathcal{O}(10^{-5})$ are distinguishable from the BH case, irrespective of the value of the eccentricity. We stress, however, that this finding should be corroborated by future parameter estimation studies.

Fast electronic readout for high-channel density scintillator-based systems is needed for radiation tracking and imaging in a wide range of applications, including nuclear physics, nuclear security and nonproliferation. Programmable electronics, like FPGAs and ASICs, provide a fast way of conditioning and processing the signal in real time. In this paper, we present a pulse shape discrimination (PSD) method based on the shaping circuit of a commercially available ASIC, the Citiroc1A by CAEN Technologies. We used two different shaping times per detector channel to calculate a shaping parameter that enables PSD. Using our new method, neutron and gamma-ray pulses detected by a d$_{12}$-stilbene scintillator can be effectively discriminated at light output values greater than 0.15 MeVee. While not achieving the PSD performance of traditional offline charge integration, our method does not require the transfer of data to a separate system for further processing and enables the direct deployment of high-channel density multi-particle detection systems. Moreover, the availability of a wider range of shaping times than those on the Citiroc1A can potentially further improve the PSD performance., Comment: 20 pages, 17 figures

We compute the vacuum metric generated by a generic rotating object in arbitrary dimensions up to third post-Minkowskian order by computing the classical contribution of scattering amplitudes describing the graviton emission by massive spin-1 particles up to two loops. The solution depends on the mass, angular momenta, and on up to two parameters related to generic quadrupole moments. In $D=4$ spacetime dimensions, we recover the vacuum Hartle-Thorne solution describing a generic spinning object to second order in the angular momentum, of which the Kerr metric is a particular case obtained for a specific mass quadrupole moment dictated by the uniqueness theorem. At the level of the effective action, the case of minimal couplings corresponds to the Kerr black hole, while any other mass quadrupole moment requires non-minimal couplings. In $D>4$, the absence of black-hole uniqueness theorems implies that there are multiple spinning black hole solutions with different topology. Using scattering amplitudes, we find a generic solution depending on the mass, angular momenta, the mass quadrupole moment, and a new stress quadrupole moment which does not exist in $D=4$. As special cases, we recover the Myers-Perry and the single-angular-momentum black ring solutions, to third and first post-Minkowksian order, respectively. Interestingly, at variance with the four dimensional case, none of these solutions corresponds to the minimal coupling in the effective action. This shows that, from the point of view of scattering amplitudes, black holes are the "simplest" General Relativity vacuum solutions only in $D=4$., Comment: 52 pages, Mathematica notebook attached, references added in v2, a note in the conclusions has been added in v2, typos corrected in v3

Jets from early stages of heavy-ion collisions undergo modified showering in quark-gluon plasma (QGP) relative to vacuum due to jet-medium interactions, which can be measured using observables like differential jet shape and generalized angularities. Differential jet shape ($\rho(\mathbf{R})$) encodes radially differential information about jet broadening and has shown an average migration of charged energy away from the axes of quenched jets from Pb+Pb collisions at the LHC. Measurements of generalized angularities in presence of the medium from Pb+Pb collisions at the LHC show harder, or more quark-like jet fragmentation relative to vacuum. Measuring these distributions in heavy-ion collisions at RHIC will help us further characterize jet-medium interactions in a phase-space region complementary to that of the LHC. In these proceedings, we present the first fully corrected measurements of $\rho(\mathbf{R})$, jet girth (g), momentum dispersion ($p_T^D$) and momentum difference of leading and subleading constituent particles (LeSub) observables, using hard-core jets in $p$+$p$ collisions at $\sqrt{s} = 200$ GeV, collected by the STAR experiment. Finally, the data are compared with model calculations and the physics implications are discussed.

Jets are produced in early stages of heavy-ion collisions and undergo modified showering in the quark-gluon plasma (QGP) medium relative to a vacuum case. These modifications can be measured using observables like jet momentum profile and generalized angularities to study the details of jet-medium interactions. Jet momentum profile ($\rho(r)$) encodes radially differential information about jet broadening and has shown migration of charged energy towards the jet periphery in Pb+Pb collisions at the LHC. Measurements of generalized angularities (girth $g$ and momentum dispersion $p_T^D$) and LeSub (difference between leading and subleading constituents) from Pb+Pb collisions at the LHC show harder, or more quark-like jet fragmentation, in the presence of the medium. Measuring these distributions in heavy-ion collisions at RHIC will help us further characterize the jet-medium interactions in a phase-space region complimentary to that of the LHC. In this contribution, we present the first measurements of fully corrected $g$, $p_T^D$ and LeSub observables using hard-core jets in Au+Au collisions at $\sqrt{s_{\rm NN}}=200$ GeV, collected by the STAR experiment at RHIC., Comment: XXXth International Conference on Ultra-relativistic Nucleus-Nucleus Collisions (Quark Matter 2023), Houston, Texas, USA

The detection of a subsolar object in a compact binary merger is regarded as one of the smoking gun signatures of a population of primordial black holes~(PBHs). We critically assess whether these systems could be distinguished from stellar binaries, for example composed of white dwarfs or neutron stars, which could also populate the subsolar mass range. At variance with PBHs, the gravitational-wave signal from stellar binaries is affected by tidal effects, which dramatically grow for moderately compact stars as those expected in the subsolar range. We forecast the capability of constraining tidal effects of putative subsolar neutron star binaries with current and future LIGO-Virgo-KAGRA (LVK) sensitivities as well as next-generation experiments. We show that, should LVK O4 run observe subsolar neutron-star mergers, it could measure the (large) tidal effects with high significance. In particular, for subsolar neutron-star binaries, O4 and O5 projected sensitivities would allow measuring the effect of tidal disruption on the waveform in a large portion of the parameter space, also constraining the tidal deformability at ${\cal O}(10\%)$ level, thus excluding a primordial origin of the binary. Vice versa, for subsolar PBH binaries, model-agnostic upper bounds on the tidal deformability can rule out neutron stars or more exotic competitors. Assuming events similar to the subthreshold candidate SSM200308 reported in LVK O3b data are PBH binaries, O4 projected sensitivity would allow ruling out the presence of neutron-star tidal effects at $\approx 3 \sigma$ C.L., thus strengthening the PBH hypothesis. Future experiments would lead to even stronger ($>5\sigma$) conclusions on potential discoveries of this kind., Comment: 15 pages, 15 figures. v2: minor changes, matching published version

We develop a fully analytical waveform model for precessing binaries with arbitrary spin vectors using post-Newtonian~(PN) theory in the extreme mass-ratio limit and a hierarchical multi-scale analysis. The analytical model incorporates leading PN order spin precession dynamics from spin-orbit, spin-spin, and quadrupole-monopole couplings, and 2PN order dissipative dynamics truncated to first order in the mass ratio $q \ll 1$. Due to the pure analytic nature of the model, the framework developed herein can readily be extended to both higher PN and higher-$q$ order. Although the PN series is asymptotic to this limit, our results can be used to estimate how precession affects the measurability of certain binary parameters, and to inform and compare with other waveform approximants, such as effective-one-body models, hybrid waveforms, and self-force calculations., Comment: 29 pages, 10 figures, published version

The Laser Interferometer Space Antenna (LISA) is the first scientific endeavour to detect and study gravitational waves from space. LISA will survey the sky for Gravitational Waves in the 0.1 mHz to 1 Hz frequency band which will enable the study of a vast number of objects ranging from Galactic binaries and stellar mass black holes in the Milky Way, to distant massive black-hole mergers and the expansion of the Universe. This definition study report, or Red Book, presents a summary of the very large body of work that has been undertaken on the LISA mission over the LISA definition phase., Comment: 155 pages, with executive summary and table of contents

Embodied agents capable of complex physical skills can improve productivity, elevate life quality, and reshape human-machine collaboration. We aim at autonomous training of embodied agents for various tasks involving mainly large foundation models. It is believed that these models could act as a brain for embodied agents; however, existing methods heavily rely on humans for task proposal and scene customization, limiting the learning autonomy, training efficiency, and generalization of the learned policies. In contrast, we introduce a brain-body synchronization ({\it BBSEA}) scheme to promote embodied learning in unknown environments without human involvement. The proposed combines the wisdom of foundation models (``brain'') with the physical capabilities of embodied agents (``body''). Specifically, it leverages the ``brain'' to propose learnable physical tasks and success metrics, enabling the ``body'' to automatically acquire various skills by continuously interacting with the scene. We carry out an exploration of the proposed autonomous learning scheme in a table-top setting, and we demonstrate that the proposed synchronization can generate diverse tasks and develop multi-task policies with promising adaptability to new tasks and configurations. We will release our data, code, and trained models to facilitate future studies in building autonomously learning agents with large foundation models in more complex scenarios. More visualizations are available at \href{https://bbsea-embodied-ai.github.io}{https://bbsea-embodied-ai.github.io}

We consider a theory in which a real scalar field is Yukawa-coupled to a fermion and has a potential with two non-degenerate vacua. If the coupling is sufficiently strong, a collection of N fermions deforms the true vacuum state, creating energetically-favored false-vacuum pockets in which fermions are trapped. We embed this model within General Relativity and prove that it admits self-gravitating compact objects where the scalar field acquires a non-trivial profile due to non-perturbative effects. We discuss some applications of this general mechanism: i) neutron soliton stars in low-energy effective QCD, which naturally happen to have masses around 2 solar masses and radii around 10 km even without neutron interactions; ii) Higgs false-vacuum pockets in and beyond the Standard Model; iii) dark soliton stars in models with a dark sector. In the latter two examples, we find compelling solutions naturally describing centimeter-size compact objects with masses around 10^-6 solar masses, intriguingly in a range compatible with the OGLE+HSC microlensing anomaly. Besides these interesting examples, the mechanism of non-perturbative vacuum scalarization may play a role in various contexts in and beyond the Standard Model, providing a support mechanism for new compact objects that can form in the early universe, can collapse into primordial black holes through accretion past their maximum mass, and serve as dark matter candidates., Comment: 12 pages, 6 figures. v2: matches version accepted in PRD

With the widespread use of NeRF-based implicit 3D representation, the need for camera localization in the same representation becomes manifestly apparent. Doing so not only simplifies the localization process -- by avoiding an outside-the-NeRF-based localization -- but also has the potential to offer the benefit of enhanced localization. This paper studies the problem of localizing cameras in NeRF using a diffusion model for camera pose adjustment. More specifically, given a pre-trained NeRF model, we train a diffusion model that iteratively updates randomly initialized camera poses, conditioned upon the image to be localized. At test time, a new camera is localized in two steps: first, coarse localization using the proposed pose diffusion process, followed by local refinement steps of a pose inversion process in NeRF. In fact, the proposed camera localization by pose diffusion (CaLDiff) method also integrates the pose inversion steps within the diffusion process. Such integration offers significantly better localization, thanks to our downstream refinement-aware diffusion process. Our exhaustive experiments on challenging real-world data validate our method by providing significantly better results than the compared methods and the established baselines. Our source code will be made publicly available.

In this work, we address the challenge of deploying Neural Radiance Field (NeRFs) in Simultaneous Localization and Mapping (SLAM) under the condition of lacking depth information, relying solely on RGB inputs. The key to unlocking the full potential of NeRF in such a challenging context lies in the integration of real-world priors. A crucial prior we explore is the binary opacity prior of 3D space with opaque objects. To effectively incorporate this prior into the NeRF framework, we introduce a ternary-type opacity (TT) model instead, which categorizes points on a ray intersecting a surface into three regions: before, on, and behind the surface. This enables a more accurate rendering of depth, subsequently improving the performance of image warping techniques. Therefore, we further propose a novel hybrid odometry (HO) scheme that merges bundle adjustment and warping-based localization. Our integrated approach of TT and HO achieves state-of-the-art performance on synthetic and real-world datasets, in terms of both speed and accuracy. This breakthrough underscores the potential of NeRF-SLAM in navigating complex environments with high fidelity., Comment: IROS 2024

The Bird-Eye-View (BEV) is one of the most widely-used scene representations for visual perception in Autonomous Vehicles (AVs) due to its well suited compatibility to downstream tasks. For the enhanced safety of AVs, modeling perception uncertainty in BEV is crucial. Recent diffusion-based methods offer a promising approach to uncertainty modeling for visual perception but fail to effectively detect small objects in the large coverage of the BEV. Such degradation of performance can be attributed primarily to the specific network architectures and the matching strategy used when training. Here, we address this problem by combining the diffusion paradigm with current state-of-the-art 3D object detectors in BEV. We analyze the unique challenges of this approach, which do not exist with deterministic detectors, and present a simple technique based on object query interpolation that allows the model to learn positional dependencies even in the presence of the diffusion noise. Based on this, we present a diffusion-based DETR model for object detection that bears similarities to particle methods. Abundant experimentation on the NuScenes dataset shows equal or better performance for our generative approach, compared to deterministic state-of-the-art methods. Our source code will be made publicly available.

This paper develops and analyses semi-discrete numerical method for two dimensional Vlasov-Stokes' system with periodic boundary condition. The method is based on coupling of semi-discrete discontinuous Galerkin method for the Vlasov equation with discontinuous Galerkin scheme for the stationary incompressible Stokes' equation. The proposed method is both mass and momentum conservative. Since it is difficult to establish non-negativity of the discrete local density, the generalized discrete Stokes' operator become non-coercive and indefinite and under smallness condition on the discretization parameter, optimal error estimates are established with help of a modified the Stokes' projection to deal with Stokes' part and with the help of a special projection to tackle the Vlasov part. Finally, numerical experiments based on the dG method combined with a splitting algorithm are performed., Comment: 26 pages, 4figure

Understanding the decision-making process of drivers is one of the keys to ensuring road safety. While the driver intent and the resulting ego-motion trajectory are valuable in developing driver-assistance systems, existing methods mostly focus on the motions of other vehicles. In contrast, we focus on inferring the ego trajectory of a driver's vehicle using their gaze data. For this purpose, we first collect a new dataset, GEM, which contains high-fidelity ego-motion videos paired with drivers' eye-tracking data and GPS coordinates. Next, we develop G-MEMP, a novel multimodal ego-trajectory prediction network that combines GPS and video input with gaze data. We also propose a new metric called Path Complexity Index (PCI) to measure the trajectory complexity. We perform extensive evaluations of the proposed method on both GEM and DR(eye)VE, an existing benchmark dataset. The results show that G-MEMP significantly outperforms state-of-the-art methods in both benchmarks. Furthermore, ablation studies demonstrate over 20% improvement in average displacement using gaze data, particularly in challenging driving scenarios with a high PCI. The data, code, and models can be found at https://eth-ait.github.io/g-memp/.

Flow coefficients ($v_2$ and $v_3$) are measured in high-multiplicity $p$+Au, $d$+Au, and $^{3}$He$+$Au collisions at a center-of-mass energy of $\sqrt{s_{_{\mathrm{NN}}}}$ = 200 GeV using the STAR detector. The measurements utilize two-particle correlations with a pseudorapidity requirement of $|\eta| <$ 0.9 and a pair gap of $|\Delta\eta|>1.0$. The primary focus is on analysis methods, particularly the subtraction of non-flow contributions. Four established non-flow subtraction methods are applied to determine $v_n$, validated using the HIJING event generator. $v_n$ values are compared across the three collision systems at similar multiplicities; this comparison cancels the final state effects and isolates the impact of initial geometry. While $v_2$ values show differences among these collision systems, $v_3$ values are largely similar, consistent with expectations of subnucleon fluctuations in the initial geometry. The ordering of $v_n$ differs quantitatively from previous measurements using two-particle correlations with a larger rapidity gap, which, according to model calculations, can be partially attributed to the effects of longitudinal flow decorrelations. The prospects for future measurements to improve our understanding of flow decorrelation and subnucleonic fluctuations are also discussed., Comment: 29 pages, 25 figures