October 2024 arXiv papers — page 54

Clémence Chanavat, Amar Hadzihasanovic

Diagrammatic sets admit a notion of internal equivalence in the sense of coinductive weak invertibility, with similar properties to its analogue in strict $\omega$-categories. We construct a model structure whose fibrant objects are diagrammatic sets in which every round pasting diagram is equivalent to a single cell -- its weak composite -- and propose them

Jia-Bao Wang, Zi-Hao Dong, Yi Zhang

Spontaneous symmetry breaking generally circumvents one-dimensional systems with local interactions in thermal equilibrium. Here, we analyze a category of one-dimensional Hermitian models via local non-Hermitian constructions. Notably, spontaneous symmetry breaking and long-range order may emerge at finite temperatures in such systems under periodic boundary

Tal Schwartzman

Embezzlement of entanglement is the counterintuitive process in which entanglement is extracted from a resource system using local unitary operations, with almost no detectable change in the resource's state. It has recently been argued that any state of a relativistic quantum field theory can serve as a resource for perfect embezzlement. We study the circui

D. M. Rowan, Todd A. Thompson, C. S. Kochanek, G. Li Causi

We report the results from a pilot study to search for black holes and other dark companions in binary systems using direct imaging with SHARK-VIS and the iLocater pathfinder "Lili" on the Large Binocular Telescope. Starting from known single-lined spectroscopic binaries, we select systems with high mass functions that could host dark companions and whose sp

Sergio Benvenuti, Riccardo Comi, Sara Pasquetti

Recently it was shown that mirror duals of 3d and 4d theories with four super-charges can be described by generalized quiver theories, constructed using strongly coupled SCFTs as elementary building blocks that replace and improve standard bifundamentals. In this work we study and extend the family of such improved bifundamentals and discuss the network of s

Basabendu Barman, Arindam Basu, Debasish Borah, Amit Chakraborty

We study the generation of baryon asymmetry as well as dark matter (DM) in an extended reheating period after the end of slow-roll inflation. Within the regime of perturbative reheating, we consider different monomial potential of the inflaton field during reheating era. The inflaton condensate reheats the Universe by decaying into the Standard Model (SM) ba

Pavlo Plotko, Walter Winter, Cecilia Lunardini, Chengchao Yuan

We revisit the Ultra-High-Energy Cosmic Ray (UHECRs) production in Tidal Disruption Events (TDEs) in the light of recent neutrino-TDE associations. We use an isotropically emitting source-propagation model, which has been developed to describe the neutrino production in AT2019dsg, AT2019fdr, and AT2019aalc. These TDEs have strong dust echoes in the infrared

William R. Coulton, Adriaan J. Duivenvoorden, Zachary Atkins, Nicholas Battaglia

The high electron temperature in galaxy clusters ($>1\,$keV or $>10^7\,$K) leads to corrections at the level of a few percent in their thermal Sunyaev-Zeldovich effect signatures. Both the size and frequency dependence of these corrections, which are known as relativistic temperature corrections, depend upon the temperature of the objects. In this work we ex

Hooman Davoudiasl, Peter B. Denton

Current laboratory bounds imply that protons are extremely long-lived. However, this conclusion may not hold for all time and in all of space. We find that the proton lifetime can be $\sim 15$ orders of magnitude shorter in the relatively recent past on Earth, or at the present time elsewhere in the Milky Way. A number of terrestrial and astrophysical constr

Pedro Vicente Marto, Umut Gürsoy, Guim Planella Planas

We investigate entanglement entropy in $3d$ $\mathcal{N}=2$ superconformal field theories from two different perspectives. We first confirm that the dependence of supersymmetric entanglement entropy (as defined in arXiv:1306.2958) on the entangling region is purely topological. We then compute entanglement entropy of $3d$ $\mathcal{N}=2$ superconformal gauge

Paz Beniamini, Pawan Kumar

We propose a scenario that can describe a broad range of FRB phenomenology, from non-repeating bursts to highly prolific repeaters. Coherent radio waves in these bursts are produced in the polar cap region of a magnetar, where magnetic field lines are open. The angle between the rotation and magnetic axes, relative to the angular size of the polar cap region

A. Ferrara, A. Pallottini, L. Sommovigo

The properties of luminous, blue (a.k.a. Blue Monsters), super-early galaxies at redshift $z>10$ have been successfully explained by the attenuation-free model (AFM) in which dust is pushed to kpc-scales by radiation-driven outflows. As an alternative to AFM, here we assess whether *attenuation-free* conditions can be replaced by a *dust-free* scenario in wh

Kara Farnsworth, Kurt Hinterbichler, Samanta Saha

In $D$ dimensional de Sitter space, a scalar field has an infinite tower of special tachyonic mass values at which enhanced shift symmetries appear. After modding out by these shift symmetries, these fields correspond to the unitary irreducible representations of the de Sitter group known as the discrete series. We show that in $D=2$ these theories have glob

Lea E. Bottini, Sakura Schafer-Nameki

We construct a (1+1)d gapless theory which has Haagerup $\mathcal{H}_3$ symmetry. The construction relies on the recent exploration of the categorical Landau paradigm applied to fusion category symmetries. First, using the Symmetry Topological Field Theory, we construct all gapped phases with Haagerup symmetry. Extending this construction to gapless phases,

Tatsuya Akiba, Smadar Naoz, Ann-Marie Madigan

The Galactic Center hosts a rotating disk of young stars between 0.05 and 0.5 pc of Sgr A*. The ``S-stars'' at a distance $<0.04$ pc, however, are on eccentric orbits with nearly isotropically distributed inclinations. The dynamical origin of the S-star cluster has remained a theoretical challenge. Using a series of $N$-body simulations, we show that a recen

Artur Czerwinski

Quantum state tomography (QST) is an essential technique for characterizing quantum states. However, practical implementations of QST are significantly challenged by factors such as shot noise, attenuation, and Raman scattering, especially when photonic qubits are transmitted through optical fibers alongside classical signals. In this paper, we present a num

Gaurav Tenkila, Romain Vasseur, Andrew C. Potter

The edge of a quantum critical system can exhibit multiple distinct types of boundary criticality. We use a numerical real-space renormalization group (RSRG) to study the boundary criticality of a 2d quantum Ising model with random exchange couplings and transverse fields, whose bulk exhibits an infinite randomness critical point. This approach enables an as

A. P. Costa, A. L. B. Ribeiro, R. R. de Carvalho, J. A. Benevides

In this work we improve the dynamic-evolutionary framework of two massive clusters at intermediate redshifts: Cl 0024+17 at $z \sim 0.4$ and MS 0451-03 at $z \sim 0.5$. The spectroscopic galaxy members were selected from Moran et al. (2007a), which combine optical and UV imaging with spectroscopy. Using a set of dynamic estimators with different approaches,

Pietro Benetti Genolini, Jerome P. Gauntlett, Yusheng Jiao, Alice Lüscher

We introduce a general class of toric gravitational instantons in $D=4$, $\mathcal{N}=2$ gauged supergravity, namely Euclidean supersymmetric solutions with $U(1)^2$ isometry. Such solutions are specified by a "supergravity labelled polytope", where the labels encode the 4-manifold topology, the choice of magnetic fluxes, and certain signs associated to the

Jorick S. Vink

Recent studies of high-redshift galaxies using JWST, such as GN-z11 revealed highly elevated levels of nitrogen (N). This phenomenon extends to gravitationally-lensed galaxies like the Sunburst Arc at z = 2.37, as well as to globular clusters (GCs). We propose that this originates from the presence of very massive stars (VMSs) with masses ranging from 100 to

Xin Fei, Wenzhao Zheng, Yueqi Duan, Wei Zhan

We propose PixelGaussian, an efficient feed-forward framework for learning generalizable 3D Gaussian reconstruction from arbitrary views. Most existing methods rely on uniform pixel-wise Gaussian representations, which learn a fixed number of 3D Gaussians for each view and cannot generalize well to more input views. Differently, our PixelGaussian dynamically

Wen Wang, Qiuyu Wang, Kecheng Zheng, Hao Ouyang

We propose Framer for interactive frame interpolation, which targets producing smoothly transitioning frames between two images as per user creativity. Concretely, besides taking the start and end frames as inputs, our approach supports customizing the transition process by tailoring the trajectory of some selected keypoints. Such a design enjoys two clear b

Ling-Hao Chen, Shunlin Lu, Wenxun Dai, Zhiyang Dou

This research delves into the problem of interactive editing of human motion generation. Previous motion diffusion models lack explicit modeling of the word-level text-motion correspondence and good explainability, hence restricting their fine-grained editing ability. To address this issue, we propose an attention-based motion diffusion model, namely MotionC

Sara Ghaboura, Ahmed Heakl, Omkar Thawakar, Ali Alharthi

Recent years have witnessed a significant interest in developing large multimodal models (LMMs) capable of performing various visual reasoning and understanding tasks. This has led to the introduction of multiple LMM benchmarks to evaluate LMMs on different tasks. However, most existing LMM evaluation benchmarks are predominantly English-centric. In this wor

Jialu Li, Yuanzhen Li, Neal Wadhwa, Yael Pritch

We introduce the concept of a generative infinite game, a video game that transcends the traditional boundaries of finite, hard-coded systems by using generative models. Inspired by James P. Carse's distinction between finite and infinite games, we leverage recent advances in generative AI to create Unbounded: a game of character life simulation that is full

Hansheng Chen, Bokui Shen, Yulin Liu, Ruoxi Shi

Multi-view image diffusion models have significantly advanced open-domain 3D object generation. However, most existing models rely on 2D network architectures that lack inherent 3D biases, resulting in compromised geometric consistency. To address this challenge, we introduce 3D-Adapter, a plug-in module designed to infuse 3D geometry awareness into pretrain

Naitong Chen, Jonathan H. Huggins, Trevor Campbell

A Bayesian coreset is a small, weighted subset of a data set that replaces the full data during inference to reduce computational cost. The state-of-the-art coreset construction algorithm, Coreset Markov chain Monte Carlo (Coreset MCMC), uses draws from an adaptive Markov chain targeting the coreset posterior to train the coreset weights via stochastic gradi

David Ortiz-Perez, Manuel Benavent-Lledo, Jose Garcia-Rodriguez, David Tomás

Cognitive decline is a natural part of aging. However, under some circumstances, this decline is more pronounced than expected, typically due to disorders such as Alzheimer's disease. Early detection of an anomalous decline is crucial, as it can facilitate timely professional intervention. While medical data can help, it often involves invasive procedures. A

Luca Domenico Loiacono, Anthony Quinn, Emanuele Crisostomi, Robert Shorten

The green potential of electric vehicles (EVs) can be fully realized only if their batteries are charged using energy generated from renewable (i.e. green) sources. For logistic or economic reasons, however, EV drivers may be tempted to avoid charging stations certified as providing green energy, instead opting for conventional ones, where only a fraction of

Cristian Daniel Păduraru, Antonio Bărbălau, Radu Filipescu, Andrei Liviu Nicolicioiu

Current methods for detecting spurious correlations rely on analyzing dataset statistics or error patterns, leaving many harmful shortcuts invisible when counterexamples are absent. We introduce BEE (Bridging Explainability and Embeddings), a framework that shifts the focus from model predictions to the weight space, and to the embedding geometry underlying

Ruoshi Liu, Huy Ha, Mengxue Hou, Shuran Song

Underwater robotic manipulation faces significant challenges due to complex fluid dynamics and unstructured environments, causing most manipulation systems to rely heavily on human teleoperation. In this paper, we introduce AquaBot, a fully autonomous manipulation system that combines behavior cloning from human demonstrations with self-learning optimization

Kodai Sakurai, Wen Yin

Recently, one of the present authors noticed a stimulated emission process of bosonic dark matter via the two-body decay of a mother particle in a thermal plasma similar to the operation principle of a laser in 2301.08735. In this paper, we show that in a $2 \to 2$ process, including a bosonic final particle (e.g., an axion or dark photon), the stimulated em

Zhangheng Li, Keen You, Haotian Zhang, Di Feng

Building a generalist model for user interface (UI) understanding is challenging due to various foundational issues, such as platform diversity, resolution variation, and data limitation. In this paper, we introduce Ferret-UI 2, a multimodal large language model (MLLM) designed for universal UI understanding across a wide range of platforms, including iPhone

Yujuan Fu, Ozlem Uzuner, Meliha Yetisgen, Fei Xia

Large language models (LLMs) have demonstrated great performance across various benchmarks, showing potential as general-purpose task solvers. However, as LLMs are typically trained on vast amounts of data, a significant concern in their evaluation is data contamination, where overlap between training data and evaluation datasets inflates performance assessm

Bingcong Li, Liang Zhang, Aryan Mokhtari, Niao He

This work revisits the classical low-rank matrix factorization problem and unveils the critical role of initialization in shaping convergence rates for such nonconvex and nonsmooth optimization. We introduce Nystrom initialization, which significantly improves the global convergence of Scaled Gradient Descent (ScaledGD) in both symmetric and asymmetric matri

Shivin Dass, Jiaheng Hu, Ben Abbatematteo, Peter Stone

Many robot manipulation tasks require active or interactive exploration behavior in order to be performed successfully. Such tasks are ubiquitous in embodied domains, where agents must actively search for the information necessary for each stage of a task, e.g., moving the head of the robot to find information relevant to manipulation, or in multi-robot doma

Xiaoqiang Wang, Bang Liu

Large language models (LLMs) and large multimodal models (LMMs) have shown great potential in automating complex tasks like web browsing and gaming. However, their ability to generalize across diverse applications remains limited, hindering broader utility. To address this challenge, we present OSCAR: Operating System Control via state-Aware reasoning and Re

Junyi Chen, Di Huang, Weicai Ye, Wanli Ouyang

Spatial intelligence is the ability of a machine to perceive, reason, and act in three dimensions within space and time. Recent advancements in large-scale auto-regressive models have demonstrated remarkable capabilities across various reasoning tasks. However, these models often struggle with fundamental aspects of spatial reasoning, particularly in answeri

Larissa Inácio, Felipe S. S. Rosa, Serge Reynaud, Paulo A. Maia Neto

The Dzyaloshinskii-Lifshitz-Pitaevskii (DLP) theory of Casimir forces predicts a repulsion between two material surfaces separated by a third medium with an intermediate dielectric function. This DLP repulsion paradigm constitutes an important example with many applications. We show here that it is broken when the surfaces interact across salted water due to

R. Potapov, A. Zotov

We describe the Ruijsenaars' action-angle duality in classical many-body integrable systems through the spectral duality transformation relating the classical spin chains and Gaudin models. For this purpose, the Lax matrices of many-body systems are represented in the multi-pole (Gaudin-like) form by introducing a fictitious spectral parameter. This form of

Jihai Zhang, Meng-Han Zhang, Peigen Li, Zizhao Liu

Magnon Chern insulators (MCIs) exhibit unique topological magnon band structures featuring chiral edge states. Direct observations of the topologically protected magnon edge states have long been pursued. Here, we report the spatially resolved detection of magnon edge states in a two-dimensional ferromagnet with honeycomb lattice (single-layer chromium triio

Andrew Robert Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi

Forecasting is a critical task in decision-making across numerous domains. While historical numerical data provide a start, they fail to convey the complete context for reliable and accurate predictions. Human forecasters frequently rely on additional information, such as background knowledge and constraints, which can efficiently be communicated through nat

Fu-Yun Wang, Zhengyang Geng, Hongsheng Li

Diffusion models achieve superior generation quality but suffer from slow generation speed due to the iterative nature of denoising. In contrast, consistency models, a new generative family, achieve competitive performance with significantly faster sampling. These models are trained either through consistency distillation, which leverages pretrained diffusio

Jipeng Zhang, Jianshu Zhang, Yuanzhe Li, Renjie Pi

Large Language Models (LLMs) demonstrate strong proficiency in generating code for high-resource programming languages (HRPLs) like Python but struggle significantly with low-resource programming languages (LRPLs) such as Racket or D. This performance gap deepens the digital divide, preventing developers using LRPLs from benefiting equally from LLM advanceme

Zhiwen Fan, Jian Zhang, Wenyan Cong, Peihao Wang

Reconstructing and understanding 3D structures from a limited number of images is a well-established problem in computer vision. Traditional methods usually break this task into multiple subtasks, each requiring complex transformations between different data representations. For instance, dense reconstruction through Structure-from-Motion (SfM) involves conv

Samy Jelassi, Clara Mohri, David Brandfonbrener, Alex Gu

The Mixture-of-Experts (MoE) architecture enables a significant increase in the total number of model parameters with minimal computational overhead. However, it is not clear what performance tradeoffs, if any, exist between MoEs and standard dense transformers. In this paper, we show that as we increase the number of experts (while fixing the number of acti

V. Cianciolo

nEDMSF aims to measure the neutron electric dipole moment ($d_n$) with unprecedented precision. In this paper we explore the experiment's sensitivity when operating with an implementation of the critical dressing method in which the angle between the neutron and Helium-3 spins ($\phi_{3n}$) is subjected to a square modulation by an amount $\phi_d$ (the "dres

Yujuan Velvin Fu, Giridhar Kaushik Ramachandran, Namu Park, Kevin Lybarger

Large language models (LLMs) such as ChatGPT are fine-tuned on large and diverse instruction-following corpora, and can generalize to new tasks. However, those instruction-tuned LLMs often perform poorly in specialized medical natural language understanding (NLU) tasks that require domain knowledge, granular text comprehension, and structured data extraction

Han Wang, Eduardo Pérez, Iris A. M. Huijben, Hans van Gorp

Multidimensional data acquisition often requires extensive time and poses significant challenges for hardware and software regarding data storage and processing. Rather than designing a single compression matrix as in conventional compressed sensing, structured compressed sensing yields dimension-specific compression matrices, reducing the number of optimiza

Alexander Poremba, Yihui Quek, Peter Shor

Random classical codes have good error correcting properties, and yet they are notoriously hard to decode in practice. Despite many decades of extensive study, the fastest known algorithms still run in exponential time. The Learning Parity with Noise (LPN) problem, which can be seen as the task of decoding a random linear code in the presence of noise, has t

Jort Vincenti, Karim Abdel Sadek, Joan Velja, Matteo Nulli

Increasing the size of large language models (LLMs) has been shown to lead to better performance. However, this comes at the cost of slower and more expensive inference. Early-exiting is a promising approach for improving the efficiency of LLM inference by enabling next token prediction at intermediate layers. Yet, the large vocabulary size in modern LLMs ma

A. Rouzky, N. Eddy, M. A. Ibrahim

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 valua

Dylan Wilson

In this paper, I will introduce a new form of regression, that can adjust overfitting and underfitting through, "distance-based regression." Overfitting often results in finding false patterns causing inaccurate results, so by having a new approach that minimizes overfitting, more accurate predictions can be derived. Then I will proceed with a test of my reg

Di Shi, Qiang Zhang, Mingguo Hong, Fengyu Wang

Deep reinforcement learning (DRL) holds significant promise for managing voltage control challenges in simulated power grid environments. However, its real-world application in power system operations remains underexplored. This study rigorously evaluates DRL's performance and limitations within actual operational contexts by utilizing detailed experiments a

Zhimeng Ouyang

In this paper, we prove that solutions of the discrete NLS lattice model for $L^2$ initial data with double frequency components converge to solutions of a coupled system of cubic NLS.

Pranjal Ralegankar, Daniele Perri, Takeshi Kobayashi

Very little is known about the cosmological history from after the end of inflation until Big Bang Nucleosynthesis. Various well-motivated models predict that the universe could have undergone a period of matter domination in this early epoch. We demonstrate that if the particles causing matter domination have self-interactions, they can form halos that unde

David Blanik, José Garre-Rubio, András Molnár, Erez Zohar

Projected entangled pair states (PEPS) are very useful in the description of strongly correlated systems, partly because they allow encoding symmetries, either global or local (gauge), naturally. In recent years, PEPS with local symmetries have increasingly been used in the study of non-perturbative regimes of lattice gauge theories, most prominently as a wa

Theodore D. Drivas, Marc Nualart

We study the steady states of the Euler equations on the periodic channel or annulus. We show that if these flows are laminar (layered by closed non-contractible streamlines which foliate the domain), then they must be either parallel or circular flows. We also show that a large subset of these shear flows are isolated from non-shear stationary states. For P

Kyuhyoun Cho, Bart De Pontieu, Paola Testa

The origin of nonthermal broadening in solar spectra is one of the long-standing questions in solar physics. Various processes have been invoked - including unresolved flows, waves, and turbulent processes - but definitive answers are lacking. To investigate the physical processes responsible for nonthermal broadening, we examine its relation with the angle

Fabiana Ganem, Luã Bida Vacaro, Eduardo Correa Araujo, Leon Diniz Alves

Dengue is a climate-sensitive mosquito-borne disease with a complex transmission dynamic. Data related to climate, environmental and sociodemographic characteristics of the target population are important for project scenarios. Different datasets and methodologies have been applied to build complex models for dengue forecast, stressing the need to evaluate t

Tianyu Huang, Jingwang Ling, Shuang Zhao, Feng Xu

Walk on stars (WoSt) has shown its power in being applied to Monte Carlo methods for solving partial differential equations, but the sampling techniques in WoSt are not satisfactory, leading to high variance. We propose a guiding-based importance sampling method to reduce the variance of WoSt. Drawing inspiration from path guiding in rendering, we approximat

Emily Rauscher

This chapter provides an overview of the basic concepts foundational to atmospheric physics and chemistry. We discuss the retention of atmospheres against thermal evaporation and the global energy balance of planets. We present simple derivations of the vertical profile of an atmosphere, which may be shaped by convective and radiative transport. We then brie

Thiago da Silva

Inspired by the results obtained in \cite{SR}, in this work, we develop techniques to handle the contraction property for weak normalization and Lipschitz saturation of algebras for the following types of algebras: universally injective, integral, radicial, and unramified.

C. Gieser, J. E. Pineda, D. M. Segura-Cox, P. Caselli

Context. The formation of stars has been subject to extensive studies in the past decades from molecular cloud to protoplanetary disk scales. It is still not fully understood how the surrounding material in a protostellar system, that often shows asymmetric structures with complex kinematic properties, feeds the central protostar(s) and their disk(s). Aims.

Luca Franceschelli, Christian E. Willert, Marco Raiola, Stefano Discetti

This study explores the potential of neuromorphic Event-Based Vision (EBV) cameras for data-efficient representation of low-order model coordinates in turbulent flows. Unlike conventional imaging systems, EBV cameras asynchronously capture changes in temporal contrast at each pixel, delivering high-frequency output with reduced data bandwidth and enhanced se

Elena Bortolato, Antonio Canale

Factor Analysis has traditionally been utilized across diverse disciplines to extrapolate latent traits that influence the behavior of multivariate observed variables. Historically, the focus has been on analyzing data from a single study, neglecting the potential study-specific variations present in data from multiple studies. Multi-study factor analysis ha

Yatin Dandi, Luca Pesce, Hugo Cui, Florent Krzakala

A key property of neural networks is their capacity of adapting to data during training. Yet, our current mathematical understanding of feature learning and its relationship to generalization remain limited. In this work, we provide a random matrix analysis of how fully-connected two-layer neural networks adapt to the target function after a single, but aggr

Catherine Spurin, Sharon Ellman, Dane Sherburn, Tom Bultreys

X-ray micro-computed tomography (X-ray micro-CT) is widely employed to investigate flow phenomena in porous media, providing a powerful alternative to core-scale experiments for estimating traditional petrophysical properties such as porosity, single-phase permeability or fluid connectivity. However, the segmentation process, critical for deriving these prop

Sha Li, Revanth Gangi Reddy, Khanh Duy Nguyen, Qingyun Wang

Complex news events, such as natural disasters and socio-political conflicts, require swift responses from the government and society. Relying on historical events to project the future is insufficient as such events are sparse and do not cover all possible conditions and nuanced situations. Simulation of these complex events can help better prepare and redu

Shangyuan Ye, Shauna Rakshe, Ye Liang

Simultaneous variable selection and statistical inference is challenging in high-dimensional data analysis. Most existing post-selection inference methods require explicitly specified regression models, which are often linear, as well as sparsity in the regression model. The performance of such procedures can be poor under either misspecified nonlinear model

Matias Mustonen, Teemu Ojanen, Ali G. Moghaddam

In this work, we employ a generalized transfer matrix method that provides exact analytical and numerical solutions for lattice versions of topological models with surface termination in one direction. We construct a generalized eigenvalue equation, equivalent to the conventional transfer matrix, which neither suffers from nor requires singular (non-invertib

Wiktoria Kozyra, Kevin O&#39;Neill, Stephen M. Fleming

Confidence estimates are often &#34;detection-like&#34; - driven by positive evidence in favour of a decision. This empirical observation has been interpreted as showing that human metacognition is limited by biases or heuristics. Here, we show that Bayesian confidence estimates also exhibit heightened sensitivity to decision-congruent evidence in higher-dim

Vidhi Jain, Rishi Veerapaneni, Yonatan Bisk

We propose Audio Noise Awareness using Visuals of Indoors for NAVIgation for quieter robot path planning. While humans are naturally aware of the noise they make and its impact on those around them, robots currently lack this awareness. A key challenge in achieving audio awareness for robots is estimating how loud will the robot's actions be at a listener's

Qiqi Hou, Randall Rauwendaal, Zifeng Li, Hoang Le

Recently, 3D Gaussian Splatting (3DGS) has emerged as a significant advancement in 3D scene reconstruction, attracting considerable attention due to its ability to recover high-fidelity details while maintaining low complexity. Despite the promising results achieved by 3DGS, its rendering performance is constrained by its dependence on costly non-commutative

Vincenzo Alba

We investigate the dynamics of the R\'enyi Operator Space Entanglement ($OSE$) entropies $S_n$ across several one-dimensional integrable and chaotic models. As a paradigmatic integrable system, we first consider the so-called rule $54$ chain. Our numerical results reveal that the R\'enyi $OSE$ entropies of diagonal operators with nonzero trace saturate at lo

Tiange Liu, Nikola Surjanovic, Miguel Biron-Lattes, Alexandre Bouchard-Côté

Many common Markov chain Monte Carlo (MCMC) kernels can be formulated using a deterministic involutive proposal with a step size parameter. Selecting an appropriate step size is often a challenging task in practice; and for complex multiscale targets, there may not be one choice of step size that works well globally. In this work, we address this problem wit

Zonghao Ying, Aishan Liu, Siyuan Liang, Lei Huang

Multimodal Large Language Models (MLLMs) are showing strong safety concerns (e.g., generating harmful outputs for users), which motivates the development of safety evaluation benchmarks. However, we observe that existing safety benchmarks for MLLMs show limitations in query quality and evaluation reliability limiting the detection of model safety implication

Elias Jääsaari, Ville Hyvönen, Teemu Roos

Approximate nearest neighbor (ANN) search is a key component in many modern machine learning pipelines; recent use cases include retrieval-augmented generation (RAG) and vector databases. Clustering-based ANN algorithms, that use score computation methods based on product quantization (PQ), are often used in industrial-scale applications due to their scalabi

Jose Mathew, A Thariq

In this paper, we revisit the stability of power-law models, focusing on an alternative approach that differs significantly from the standard approaches used in studying power-law models. In the standard approach, stability is studied by reducing the system of background FRW equations to a one-dimensional system for a new background variable $X$ in terms of

Xiang Li, Jin-Du Wang, John J. Dudley, Per Ola Kristensson

The theory of swarm control shows promise for controlling multiple objects, however, scalability is hindered by cost constraints, such as hardware and infrastructure. Virtual Reality (VR) can overcome these limitations, but research on swarm interaction in VR is limited. This paper introduces a novel Swarm Manipulation interaction technique and compares it w

XuDong Wang, Shaolun Zhang, Shufan Li, Konstantinos Kallidromitis

We present SegLLM, a novel multi-round interactive reasoning segmentation model that enhances LLM-based segmentation by exploiting conversational memory of both visual and textual outputs. By leveraging a mask-aware multimodal LLM, SegLLM re-integrates previous segmentation results into its input stream, enabling it to reason about complex user intentions an

John Kallaugher, Ojas Parekh, Nadezhda Voronova

A series of work [GKK+08, Kal22, KPV24] has shown that asymptotic advantages in space complexity are possible for quantum algorithms over their classical counterparts in the streaming model. We give a simple quantum sketch that encompasses all these results, allowing them to be derived from entirely classical algorithms using our quantum sketch as a black bo

A M Muntasir Rahman, Junyi Ye, Wei Yao, Sierra S. Liu

Consider the math problem: "Lily received 3 cookies from her best friend yesterday and ate 5 for breakfast. Today, her friend gave her 3 more cookies. How many cookies does Lily have now?" Many large language models (LLMs) in previous research approach this problem by calculating the answer "1" using the equation "3 - 5 + 3." However, from a human perspectiv

Belinda Damian, Jessy Jose, Swagat R. Das, Saumya Gupta

Young star-forming regions in massive environments are ideal test beds to study the influence of surroundings on the evolution of disks around low-mass stars. We explore two distant young clusters, IC 1848-East and West located in the massive W5 complex. These clusters are unique due to their similar (distance, age, and extinction) yet distinct (stellar dens

Jiaming Qiu, Ruiqi Wang, Brooks Hu, Roch Guerin

Recent advances in machine learning and hardware have produced embedded devices capable of performing real-time object detection with commendable accuracy. We consider a scenario in which embedded devices rely on an onboard object detector, but have the option to offload detection to a more powerful edge server when local accuracy is deemed too low. Resource

Muralikrishnna G. Sethuraman, Razieh Nabi, Faramarz Fekri

Causal discovery in real-world systems, such as biological networks, is often complicated by feedback loops and incomplete data. Standard algorithms, which assume acyclic structures or fully observed data, struggle with these challenges. To address this gap, we propose MissNODAG, a differentiable framework for learning both the underlying cyclic causal graph

Shinjan Ghosh, Amit Chakraborty, Georgia Olympia Brikis, Biswadip Dey

Computational fluid dynamics (CFD) solvers employing two-equation eddy viscosity models are the industry standard for simulating turbulent flows using the Reynolds-averaged Navier-Stokes (RANS) formulation. While these methods are computationally less expensive than direct numerical simulations, they can still incur significant computational costs to achieve

Ju Chen, Chang Liu, Yun-Long Zhang

Circularly polarized gravitational wave backgrounds are predicted in many well-motivated models of inflation and phase transitions involving spontaneous parity violation. In this work, we investigate the detection of such parity-violating signals with the network of two space-borne triangular detectors. We derive the general analytical formula for the overla

Lazarus Signing

This paper is devoted to study of the limiting behaviour of an elastic material with periodically distributed rigid inclusions of size {\epsilon}, as the small parameter {\epsilon} goes to zero. We address here the case with inclusions of the same size as the period of the structure. The body in consideration here is suppose to be clamped on one part of its

I. Alekseev, V. Belov, A. Bystryakov, M. Danilov

Electron antineutrinos are emitted in the decay chains of the fission products inside a reactor core and could be used for remote monitoring of nuclear reactors. The DANSS detector is placed under the core of the 3.1 GW power reactor at the Kalinin Nuclear Power Plant (KNPP) and collects up to 5000 antineutrino events per day. DANSS measured changes of the r

Aron Kerschbaumer, Marko Ljubotina, Maksym Serbyn, Jean-Yves Desaules

Persistent revivals recently observed in Rydberg atom simulators have challenged our understanding of thermalization and attracted much interest to the concept of quantum many-body scars (QMBSs). QMBSs are non-thermal highly excited eigenstates that coexist with typical eigenstates in the spectrum of many-body Hamiltonians, and have since been reported in mu

Mingtong Zhang, Kaifeng Zhang, Yunzhu Li

Videos of robots interacting with objects encode rich information about the objects' dynamics. However, existing video prediction approaches typically do not explicitly account for the 3D information from videos, such as robot actions and objects' 3D states, limiting their use in real-world robotic applications. In this work, we introduce a framework to lear

K. M. Kolevski, M. Boninsegni

The superfluid response of nanoscale size quasi-2D He-4 droplets adsorbed on a graphite substrate is investigated by computer simulations. It is found that clusters comprising as few as 7 atoms are stable at temperatures lower than < 0.15 K. Clusters of ~20 atoms or less are liquid-like and ~100% superfluid. As the size is increased, the central region cryst

Yuchen Wang, Cameron Cianci, Irma Avdic, Rishab Dutta

Hybrid quantum-classical computing algorithms offer significant potential for accelerating the calculation of the electronic structure of strongly correlated molecules. In this work, we present the first quantum simulation of conical intersections (CIs) in a biomolecule, cytosine, using a superconducting quantum computer. We apply the Contracted Quantum Eige

Congcong Zhang, Joelene Hales, Els Peeters, Jan Cami

Polycyclic aromatic hydrocarbons (PAHs) are responsible for strong mid-IR emission features near star-forming regions. It is well known that low-metallicity environments exhibit weaker PAH emission, but it is not clear how the metallicity affects the properties of the emitting PAH population. We present a detailed study of the PAH emission in the low-metalli

Jing Peng, Yucheng Wang, Bohan Li, Yiwei Guo

Speech understanding is essential for interpreting the diverse forms of information embedded in spoken language, including linguistic, paralinguistic, and non-linguistic cues that are vital for effective human-computer interaction. The rapid advancement of large language models (LLMs) has catalyzed the emergence of Speech Large Language Models (Speech LLMs),

Caelan Garrett, Ajay Mandlekar, Bowen Wen, Dieter Fox

Imitation learning from human demonstrations is an effective paradigm for robot manipulation, but acquiring large datasets is costly and resource-intensive, especially for long-horizon tasks. To address this issue, we propose SkillMimicGen (SkillGen), an automated system for generating demonstration datasets from a few human demos. SkillGen segments human de

Leif Azzopardi, Yashar Moshfeghi

Auditing Large Language Models (LLMs) to discover their biases and preferences is an emerging challenge in creating Responsible Artificial Intelligence (AI). While various methods have been proposed to elicit the preferences of such models, countermeasures have been taken by LLM trainers, such that LLMs hide, obfuscate or point blank refuse to disclosure the

Concetta Campailla, Nicolas Forien, Lorenzo Taggi

We study the stochastic sandpile model on $\mathbb{Z}^d$ and demonstrate that the critical density is strictly less than one in all dimensions. This generalizes a previous result by Hoffman, Hu, Richey, and Rizzolo (2022), which was limited to the one-dimensional case. In addition, we show that the critical density is strictly positive on any vertex-transiti

Jussi Leinonen, Boris Bonev, Thorsten Kurth, Yair Cohen

Weather and climate data are often available at limited temporal resolution, either due to storage limitations, or in the case of weather forecast models based on deep learning, their inherently long time steps. The coarse temporal resolution makes it difficult to capture rapidly evolving weather events. To address this limitation, we introduce an interpolat