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Supersymmetry of the Robinson-Trautman solution
Get PDFThe Robinson-Trautman solution in the Einstein-Maxwell- system admits a shear-free and twist-free null geodesic congruence with a nonvanishing expansion. Restricting to the case where the Maxwell field is aligned, i.e., the spacetime is algebraically special, we provide an exhaustive classification of supersymmetric Robinson-Trautman spacetimes in the four-dimensional gauged supergravity. The differential constraints that arise from the integrability conditions of the Killing spinor equation enable us to systematically reconstruct the metric. We derive the explicit form of the Killing spinor either by directly integrating the Killing spinor equation or by casting the solution into the canonical form of supersymmetric solutions given in hep-th/0307022. In any case, the supersymmetric Robinson-Trautman solution generically exhibits a naked singularity.v2: 28 pages, 1 figure, 1 table; refs added, appendix B removed, to appear in JHE
Exposing the parton-hadron transition within jets with energy-energy correlators in pp collisions at TeV
Get PDFThis paper presents a fully-corrected measurement of the energy-energy correlator (EEC) within jets in pp collisions. The EEC traces the energy flow as a highly energetic parton undergoes a QCD shower followed by the confinement of partons into hadrons, probing the correlation function of the energy flow inside jets. The EEC observable is measured as a function of the charged particle pair angular distance, , for GeV/. In the perturbative region (large ), a good agreement between the data and a next-to-leading-log perturbative QCD calculation is observed. In the non-perturbative region (small ), the data exhibits a linear dependence. There is a transition region in between, characterized by a turnover in the EEC distribution, corresponding to the confinement process. The peak of this transition region is located at GeV/ for jets of various energies, indicating a common energy scale for the hadronization process. State-of-the-art Monte Carlo event generators are compared with the measurements, and can be used to constrain the parton shower and hadronization mechanisms.26 pages, 9 captioned figures, authors from page 20, minor fixes in figures 2, 3, A1, A2, submitted to PRL, figures at http://alice-publications.web.cern.ch/node/1132
Modern Hopfield Networks meet Encoded Neural Representations -- Addressing Practical Considerations
Get PDFContent-addressable memories such as Modern Hopfield Networks (MHN) have been studied as mathematical models of auto-association and storage/retrieval in the human declarative memory, yet their practical use for large-scale content storage faces challenges. Chief among them is the occurrence of meta-stable states, particularly when handling large amounts of high dimensional content. This paper introduces Hopfield Encoding Networks (HEN), a framework that integrates encoded neural representations into MHNs to improve pattern separability and reduce meta-stable states. We show that HEN can also be used for retrieval in the context of hetero association of images with natural language queries, thus removing the limitation of requiring access to partial content in the same domain. Experimental results demonstrate substantial reduction in meta-stable states and increased storage capacity while still enabling perfect recall of a significantly larger number of inputs advancing the practical utility of associative memory networks for real-world tasks.17 pages, 8 figures, accepted as a workshop paper at UniReps @ Neurips 202
Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration
Get PDFThe swift advancement in Multimodal LLMs (MLLMs) also presents significant challenges for effective knowledge editing. Current methods, including intrinsic knowledge editing and external knowledge resorting, each possess strengths and weaknesses, struggling to balance the desired properties of reliability, generality, and locality when applied to MLLMs. In this paper, we propose UniKE, a novel multimodal editing method that establishes a unified perspective and paradigm for intrinsic knowledge editing and external knowledge resorting. Both types of knowledge are conceptualized as vectorized key-value memories, with the corresponding editing processes resembling the assimilation and accommodation phases of human cognition, conducted at the same semantic levels. Within such a unified framework, we further promote knowledge collaboration by disentangling the knowledge representations into the semantic and truthfulness spaces. Extensive experiments validate the effectiveness of our method, which ensures that the post-edit MLLM simultaneously maintains excellent reliability, generality, and locality. The code for UniKE is available at \url{https://github.com/beepkh/UniKE}.Accepted by NeurIPS 2024 (Spotlight
First-order spin magnetohydrodynamics
Get PDFBased on recent papers, we discuss the formulation of the first-order relativistic spin magnetohydrodynamics (MHD) with the totally antisymmetric spin current and properties of the anisotropic linear waves awaken near an equilibrium configuration. We show that there appears a critical angle in the momentum direction of the linear waves, where a pair of propagating modes turns into purely diffusive modes. Due to this critical behavior, polynomial solutions do not fully capture the angle dependence of the linear waves.Contribution to the Reimei workshop 2024 in Jeju. arXiv admin note: text overlap with arXiv:2409.0709
Dessie: Disentanglement for Articulated 3D Horse Shape and Pose Estimation from Images
Get PDFIn recent years, 3D parametric animal models have been developed to aid in estimating 3D shape and pose from images and video. While progress has been made for humans, it\u27s more challenging for animals due to limited annotated data. To address this, we introduce the first method using synthetic data generation and disentanglement to learn to regress 3D shape and pose. Focusing on horses, we use text-based texture generation and a synthetic data pipeline to create varied shapes, poses, and appearances, learning disentangled spaces. Our method, Dessie, surpasses existing 3D horse reconstruction methods and generalizes to other large animals like zebras, cows, and deer. See the project website at: \url{https://celiali.github.io/Dessie/}.ACCV202
Interacting hypersurfaces and multiple scalar-tensor theories
Get PDFWe propose a novel method to construct ghost-free multiple scalar-tensor theories. The key idea is to use the geometric quantities of hypersurfaces defined by the scalar fields, rather than the covariant derivatives of scalar fields or spacetime curvature, to build the theory. This approach has proven effective in developing ghost-free scalar-tensor theories in the single-field case. When multiple scalar fields are present, each field specifies a foliation of spacelike hypersurfaces, on which we can define the normal vector, induced metric, extrinsic and intrinsic curvatures, as well as extrinsic (Lie) and intrinsic (spatial) derivatives, respectively. By employing these hypersurface geometric quantities as foundational elements, we construct the Lagrangian for interacting hypersurfaces that describes a multiple scalar-tensor theory. Given that temporal (Lie) and spatial derivatives are separated, it becomes relatively easier to control the order of time derivatives, thus helping to avoid ghost-like or unwanted degrees of freedom. In this work, we use bi-scalar-field theory as an example, focusing on polynomial-type Lagrangians. We construct monomials of hypersurface geometric quantities up to , where denotes the number of derivatives in each monomial. Additionally, we present the correspondence between expressions in terms of hypersurface quantities and those in covariant bi-scalar-tensor theory. Through a cosmological perturbation analysis of a simple model, we demonstrate that the theory propagates two tensor and two scalar degrees of freedom at the linear order in perturbations, thereby remaining free from any extra degrees of freedom.28 pages, 1 figure; v2, typos correcte
On filter design in deep convolutional neural network
Get PDFThe deep convolutional neural network (DCNN) in computer vision has given promising results. It is widely applied in many areas, from medicine, agriculture, self-driving car, biometric system, and almost all computer vision-based applications. Filters or weights are the critical elements responsible for learning in DCNN. Backpropagation has been the primary learning algorithm for DCNN and provides promising results, but the size and numbers of the filters remain hyper-parameters. Various studies have been done in the last decade on semi-supervised, self-supervised, and unsupervised methods and their properties. The effects of filter initialization, size-shape selection, and the number of filters on learning and optimization have not been investigated in a separate publication to collate all the options. Such attributes are often treated as hyper-parameters and lack mathematical understanding. Computer vision algorithms have many limitations in real-life applications, and understanding the learning process is essential to have some significant improvement. To the best of our knowledge, no separate investigation has been published discussing the filters; this is our primary motivation. This study focuses on arguments for choosing specific physical parameters of filters, initialization, and learning technic over scattered methods. The promising unsupervised approaches have been evaluated. Additionally, the limitations, current challenges, and future scope have been discussed in this paper
Consistency Diffusion Bridge Models
Get PDFDiffusion models (DMs) have become the dominant paradigm of generative modeling in a variety of domains by learning stochastic processes from noise to data. Recently, diffusion denoising bridge models (DDBMs), a new formulation of generative modeling that builds stochastic processes between fixed data endpoints based on a reference diffusion process, have achieved empirical success across tasks with coupled data distribution, such as image-to-image translation. However, DDBM\u27s sampling process typically requires hundreds of network evaluations to achieve decent performance, which may impede their practical deployment due to high computational demands. In this work, inspired by the recent advance of consistency models in DMs, we tackle this problem by learning the consistency function of the probability-flow ordinary differential equation (PF-ODE) of DDBMs, which directly predicts the solution at a starting step given any point on the ODE trajectory. Based on a dedicated general-form ODE solver, we propose two paradigms: consistency bridge distillation and consistency bridge training, which is flexible to apply on DDBMs with broad design choices. Experimental results show that our proposed method could sample to faster than the base DDBM and produce better visual quality given the same step in various tasks with pixel resolution ranging from to , as well as supporting downstream tasks such as semantic interpolation in the data space.NeurIPS 202
Computation with quantum Reed-Muller codes and their mapping onto 2D atom arrays
Get PDFWe give a fault tolerant construction for error correction and computation using two punctured quantum Reed-Muller (PQRM) codes. In particular, we consider the self-dual doubly-even code that has transversal Clifford gates (CNOT, H, S) and the triply-even code that has transversal T and CNOT gates. We show that code switching between these codes can be accomplished using Steane error correction. For fault-tolerant ancilla preparation we utilize the low-depth hypercube encoding circuit along with different code automorphism permutations in different ancilla blocks, while decoding is handled by the high-performance classical successive cancellation list decoder. In this way, every logical operation in this universal gate set is amenable to extended rectangle analysis. The CNOT exRec has a failure rate approaching at circuit-level depolarizing noise.
Furthermore, we map the PQRM codes to a 2D layout suitable for implementation in arrays of trapped atoms and try to reduce the circuit depth of parallel atom movements in state preparation. The resulting protocol is strictly fault-tolerant for the code and practically fault-tolerant for the code. Moreover, each patch requires a permutation consisting of sub-hypercube swaps only. These are swaps of rectangular grids in our 2D hypercube layout and can be naturally created with acousto-optic deflectors (AODs).
Lastly, we show for the family of QRM codes that the entire logical Clifford group can be achieved using only permutations, transversal gates, and fold-transversal gates