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Perturb-and-Revise: Flexible 3D Editing with Generative Trajectories

The fields of 3D reconstruction and text-based 3D editing have advanced significantly with the evolution of text-based diffusion models. While existing 3D editing methods excel at modifying color, tex...

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The article presents a novel approach to 3D editing using a unique method to manipulate NeRF parameters, addressing significant limitations in existing technologies. Its ability to handle extensive geometric and appearance modifications enhances its applicability, making it a potentially transformative contribution to the field. The experimental validation adds methodological rigor, and the focus on identity preservation broadens its utility.

Birth and Death of a Rose

We study the problem of generating temporal object intrinsics -- temporally evolving sequences of object geometry, reflectance, and texture, such as a blooming rose -- from pre-trained 2D foundation m...

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The study introduces a novel approach to generating temporal object dynamics using pre-trained 2D models, which is a significant advancement in computer graphics and animation. This technique reduces manual effort in 3D modeling while ensuring high-quality and temporally consistent outputs, showcasing methodological rigor and innovative use of self-supervised learning. Its implications for various applications, such as game design and virtual reality, further enhance its relevance.

Text to Blind Motion

People who are blind perceive the world differently than those who are sighted, which can result in distinct motion characteristics. For instance, when crossing at an intersection, blind individuals m...

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This paper presents a novel dataset that expands the understanding of how blind individuals move in urban environments, filling a critical gap in the existing research on human movement. The methodological approach of combining 3D motion data with textual descriptions provides a rigorous framework for future studies and contributes directly to improving safety in navigation technologies such as autonomous vehicles. The practical implications of this work for urban planning and assistive technologies further enhance its relevance.

Sparse autoencoders reveal selective remapping of visual concepts during adaptation

Adapting foundation models for specific purposes has become a standard approach to build machine learning systems for downstream applications. Yet, it is an open question which mechanisms take place d...

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The article presents a novel approach (PatchSAE) leveraging sparse autoencoders that addresses a critical gap in understanding adaptation mechanisms in foundation models. Its methodological rigor and focus on interpretable concepts enhance its impact in machine learning. The findings not only advance theoretical knowledge but also have practical applications in improving model performance in specific tasks.

MotionFlow: Attention-Driven Motion Transfer in Video Diffusion Models

Text-to-video models have demonstrated impressive capabilities in producing diverse and captivating video content, showcasing a notable advancement in generative AI. However, these models generally la...

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The article presents a significant advancement in the control of motion in text-to-video models, introducing a novel framework (MotionFlow) that leverages attention mechanisms. This innovation addresses existing limitations in generative AI, particularly in maintaining consistent motion during complex scene transformations. The methodology is robust, allowing for operation without retraining, which enhances its practical applicability and accessibility for future research. The extensive qualitative and quantitative evaluations provided add to the credibility of the findings and suggest high potential for real-world applications.

SimC3D: A Simple Contrastive 3D Pretraining Framework Using RGB Images

The 3D contrastive learning paradigm has demonstrated remarkable performance in downstream tasks through pretraining on point cloud data. Recent advances involve additional 2D image priors associated ...

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SimC3D presents a novel approach by eliminating the dependence on costly point cloud data, thereby increasing accessibility for researchers in 3D learning. Its empirical performance improvements on downstream tasks validate its effectiveness, while the simplicity of its framework allows for broader application and scalability. The use of RGB images only is a significant advancement that addresses limitations in the existing methodologies, making it particularly relevant in fields where 3D data acquisition is lacking or expensive.

Interplay of Quasi-Quantum Hall Effect and Coulomb Disorder in Semimetals

Low carrier densities in topological semimetals (TSMs) enable the exploration of novel magnetotransport in the quantum limit (QL). Reports consistent with 3D quasi-quantum Hall effect (QQHE) have repo...

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The article presents novel findings on the interplay between the quasi-quantum Hall effect and Coulomb disorder in topological semimetals, which addresses a significant gap in the understanding of the QQHE within TSMs. The experimental methodology appears rigorous, demonstrating novel tunability in a well-studied material (Cd${}_3$As${}_2$), potentially influencing future research directions in the field of magnetotransport. The predictions and discussions around defects and charged disorder are crucial for applied physics and materials science, adding both theoretical depth and experimental value to the expanding knowledge base.

Real-space chirality from crystalline topological defects in the Kitaev spin liquid

We show that certain crystalline topological defects in the gapless Kitaev honeycomb spin liquid model generate a chirality that depends in a universal manner on their emergent flux. Focusing on 5-7 d...

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The article presents a novel approach to understanding chirality in spin liquids through crystalline topological defects, which is a cutting-edge topic in condensed matter physics. The methodology involving local Chern markers and their role in the context of emergent phenomena is rigorous and potentially impactful for the study of topological phases. Its applicability to real-world materials and the identification of Chern charges suggests significant implications for future research in similar systems.

Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

We introduce InternVL 2.5, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements i...

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This article presents a significant advancement in the field of open-source multimodal large language models, addressing an important area of research that is highly relevant to AI and machine learning communities. The systematic exploration of performance trends across various dimensions (model scaling, data quality) and strong empirical evaluations establish a high methodological rigor. The demonstrated competitive performance against leading commercial models indicates the potential high applicability of this research. Its contributions to multimodal AI systems and the emphasis on data and test-time scaling further highlight its novelty and relevance, particularly in real-world applications.

Chimera: Accurate retrosynthesis prediction by ensembling models with diverse inductive biases

Planning and conducting chemical syntheses remains a major bottleneck in the discovery of functional small molecules, and prevents fully leveraging generative AI for molecular inverse design. While ea...

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The article presents a novel framework (Chimera) that significantly improves the accuracy of retrosynthesis prediction through an innovative ensembling strategy. This methodological rigor combined with promising experimental results and practical applicability in a pharmaceutical context indicates a strong potential for impacting the field of cheminformatics. The proven preference of chemists for the predictions made by Chimera lends further credibility.

DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo

Dense 3D correspondence can enhance robotic manipulation by enabling the generalization of spatial, functional, and dynamic information from one object to an unseen counterpart. Compared to shape corr...

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The article introduces DenseMatcher, a novel and effective approach to 3D semantic correspondence that shows significant improvements over prior methods. Its application in robotic manipulation and capability to generalize across unseen object categories makes it particularly impactful. The creation of a new dataset enhances its relevance, and the experimental results underline the method's robustness and practical significance.

Amplitudes for Hawking Radiation

We obtain the Hawking spectrum by exponentiating a series of Feynman diagrams describing a scalar field scattering through a collapse background. Our approach is rooted in semiclassical methods of sca...

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The article presents a novel application of semiclassical methods within the context of Hawking radiation, which is a topic of great significance in theoretical physics. The use of Feynman diagrams to articulate the Hawking spectrum provides a fresh perspective, suggesting potential new avenues for research on black holes and quantum field theory. The calculated one-loop corrections add depth to the analysis and could lead to more accurate models of black hole behavior. However, the complexity of the methods and the specification to a scalar field may limit its immediate applicability across broader contexts. Overall, the methodological rigor and potential implications for future research are substantial.

Locomotion of a Scallop-Inspired Swimmer in Granular Matter

Understanding swimming in soft yielding media is challenging due to their complex deformation response to the swimmer's motion. We experimentally show that a scallop-inspired swimmer with reciproc...

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This article presents novel insights into locomotion mechanisms of scallop-inspired swimmers in granular media, challenging established theories such as the scallop theorem. Its experimental approach combining X-ray tomography and laser profilometry indicates strong methodological rigor. The findings open pathways for understanding locomotion in non-Newtonian fluids, which could have significant implications in bio-inspired engineering and fluid dynamics research.

Reinforcement Learning: An Overview

This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement learning and sequential decision making, covering value-based RL, policy-gradient methods, model-based met...

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This article provides a comprehensive overview of reinforcement learning (RL), which is a rapidly evolving field with widespread applications. The inclusion of various methods, such as value-based and policy-gradient approaches, demonstrates a broad understanding of current methodologies. While it serves as a useful introduction, it may lack the depth and novelty needed for groundbreaking contributions, as it may not present original findings or experimental results. Nonetheless, its up-to-date nature and breadth make it a valuable resource for newcomers and seasoned researchers alike.

Mind the Time: Temporally-Controlled Multi-Event Video Generation

Real-world videos consist of sequences of events. Generating such sequences with precise temporal control is infeasible with existing video generators that rely on a single paragraph of text as input....

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The article introduces a novel approach (MinT) to generate multi-event videos with precise temporal control, addressing a significant gap in current video generation capabilities. The use of a new time-based positional encoding method (ReRoPE) and fine-tuning on temporally grounded data exemplifies methodological rigor and provides a strong foundation for advancing the field of video generation. This innovation not only improves the quality of generated videos but also opens up new possibilities for applications demanding temporal precision, marking it as a potential cornerstone for future research.

Localization of unique factorization semidomains

A semidomain is a subsemiring of an integral domain. Within this class, a unique factorization semidomain (UFS) is characterized by the property that every nonzero, nonunit element can be factored int...

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The article presents significant findings related to unique factorization semidomains, a specialized area in algebra. Its exploration of the localization of UFSs enriches current algebraic understanding, and its connection to a conjecture highlights its relevance. It employs rigorous mathematical methods, which strengthens its overall contribution to the field. The results are sufficiently novel and applicable to ongoing research in commutative algebra.

Machine-Learning Electron Dynamics with Moment Propagation Theory: Application to Optical Absorption Spectrum Computation using Real-Time TDDFT

We present an application of our new theoretical formulation of quantum dynamics, moment propagation theory (MPT) (Boyer et al., J. Chem. Phys. 160, 064113 (2024)), for employing machine-learning tech...

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This paper presents a novel integration of machine learning with moment propagation theory (MPT) to enhance the simulation of electron dynamics, specifically concerning optical absorption spectra. The methodological rigor in combining these advanced techniques suggests a significant step forward in computational quantum mechanics. The application to a variety of systems, including both isolated molecules and condensed matter, enhances its relevance across multiple contexts and suggests broader usability of the approach.

A Machine Learning-Based Approach For Detecting Malicious PyPI Packages

Background. In modern software development, the use of external libraries and packages is increasingly prevalent, streamlining the software development process and enabling developers to deploy featur...

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The article presents a novel and timely solution to a pressing issue in software development: the identification of malicious packages within the PyPI ecosystem. The use of machine learning combined with static analysis for this purpose is innovative and demonstrates a high level of methodological rigor. The high F1-measure score of 0.94 indicates strong effectiveness, suggesting that the proposed method is not only theoretically sound but also practically applicable. This could significantly influence future research in automated security measures in software development.

Using the helium triplet as a tracer of the physics of giant planet outflows

Hydrodynamic outflows, such as those observed escaping close-in gas giant planets, are not isothermal in structure. Their highly ionized nature allows them to cool adiabatically at distances beyond se...

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The article addresses a niche but significant aspect of planetary science by exploring the helium triplet's role as a tracer for the physics of gas giant outflows. By presenting novel computational simulations alongside observations from HD 189733b, it combines theoretical and empirical approaches in a rigorous manner. Its findings could offer insights into the hydrodynamics of close-in gas giant atmospheres, although the need for further observational validation slightly lowers its immediate impact.

Godbillon-Vey classes of regular Jacobi manifolds

The notion of a Jacobi manifold is a natural generalization of that of a Poisson manifold. A Jacobi manifold has a natural foliation in which each leaf has either a contact structure or a locally conf...

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The article presents a novel extension of existing concepts in differential geometry, focusing on Jacobi manifolds, which are significant in both mathematical physics and the theory of integrable systems. The explicit expression of the Godbillon-Vey class for Jacobi manifolds enhances understanding and could inspire future work in classifying manifolds. Moreover, the methodological rigor indicates a thorough exploration of the implications of regular foliations in high-dimensional geometric contexts.