November 2024 arXiv papers — page 152

Lily Newkirk, Eve Armstrong, A. Baha Balantekin, Adam Burrows

Neutrino flavor evolution is critical for understanding the physics of dense astrophysical regimes, including core-collapse supernovae (CCSN). Powerful numerical integration codes exist for simulating these environments, yet a complete understanding of the inherent nonlinearity of collective neutrino flavor oscillations and how it fits within the overall fra

Massimiliano Polichetti, Armando Galluzzi, Rohit Kumar, Amit Goyal

We provide comments on article titled "Procedures for proper validation of record critical current density claims" by Chiara Tarantini and David C. Larbalestier, arXiv:2410.22195. We respond to claims and assertions that the expressions for calculation of $J_{c,mag}$ from magnetic moments proposed in Polichetti et al. 2024, arXiv:2410.09197, are incorrect. W

S. Varona, S. Saner, O. Băzăvan, G. Araneda

We show that recent experiments in hybrid qubit-oscillator devices that measure the phase-space characteristic function of the oscillator via the qubit can be seen through the lens of functional calculus and path integrals, drawing a clear analogy with the generating functional of a quantum field theory. This connection suggests an expansion of the character

Agnibh Dasgupta, Abdullah Tanvir, Xin Zhong

Watermarking the outputs of large language models (LLMs) is critical for provenance tracing, content regulation, and model accountability. Existing approaches often rely on access to model internals or are constrained by static rules and token-level perturbations. Moreover, the idea of steering generative behavior via prompt-based instruction control remains

Pierre-Yves C. R. Taunay, Willca Villafana, Sangeeta P. Vinoth, Igor Kaganovich

A parametric study is conducted to quantify the effect of the keeper electrode geometry on the neutral flow quantities within orificed hollow cathodes, prior to cathode ignition. The keeper impinges directly on the flow out of the cathode orifice and its geometry strongly influences the product between the pressure in the orifice-keeper region and the distan

Francesco Parisen Toldin, Abijith Krishnan, Max A. Metlitski

Recent advances in boundary critical phenomena have led to the discovery of a new surface universality class in the three-dimensional $O(N)$ model. The newly found ``extraordinary-log" phase can be realized on a two-dimensional surface for $N< N_c$, with $N_c>3$, and on a plane defect embedded into a three-dimensional system, for any $N$. One of the key feat

Ibrahim Said Ahmad, Antonios Anastasopoulos, Ondřej Bojar, Claudia Borg

This paper reports on the shared tasks organized by the 21st IWSLT Conference. The shared tasks address 7 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, dialect and low-resource speech translation, and Indic languages. The shared tasks attracted 18 te

Eva Maxfield Brown, Cailean Osborne, Peter Cihon, Moritz Böhmecke-Schwafert

Existing innovation metrics inadequately capture software innovation, creating blind spots for researchers and policymakers seeking to understand and foster technological innovation in an increasingly software-defined economy. This paper introduces a novel measure of software innovation based on open source software (OSS) development activity on GitHub. We e

Odelia Teboul, Hagai Perets

Stars wandering too close to supermassive black holes (SMBHs) can be ripped apart by the tidal forces of the black hole. Recent optical surveys have revealed that E+A galaxies are overrepresented by a factor $\sim $ 30, while green galaxies are overrepresented in both optical and infrared surveys. Different stellar models have been proposed to explain this T

Daniel C. Castro, Aurelia Bustos, Shruthi Bannur, Stephanie L. Hyland

Radiology report generation (RRG) aims to create free-text radiology reports from clinical imaging. Grounded radiology report generation (GRRG) extends RRG by including the localisation of individual findings on the image. Currently, there are no manually annotated chest X-ray (CXR) datasets to train GRRG models. In this work, we present a dataset called Pad

Carlo Nipoti, Raffaele Pascale, José María Arroyo-Polonio

The dwarf spheroidal galaxies (dSphs) satellites of the Milky Way (MW) are nearby astrophysical laboratories to study the nature of dark matter (DM). We present some properties of the DM halos of the three classical dSphs Fornax, Sculptor and Leo I, obtained using dynamical models based on distribution functions depending on the action integrals. In particul

Derek Perera, John H Miller, Liliya L. R. Williams, Jori Liesenborgs

The increasingly large numbers of multiple images in cluster-scale gravitational lenses have allowed for tighter constraints on the mass distributions of these systems. Most lens models have progressed alongside this increase in image number. The general assumption is that these improvements would result in lens models converging to a common solution, sugges

Yang Hu, Suhail Dhawan

Inferring spatial curvature of the Universe with high-fidelity is a longstanding interest in cosmology. However, the strong degeneracy between dark energy equation-of-state parameter $w$ and curvature density parameter $\Omega_{\rm K}$ has always been a hurdle for precision measurements of curvature from late-universe probes. With the imminent commissioning

Christian Svingen Johnsen, Asle Sudbø

We consider the spin dynamics of a normal metal-ferromagnet heterostructure, with emphasis on spin-nutation terms arising from a dynamical Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction. We find that the spin-nutation term is anisotropic in spin space due to the broken time-reversal symmetry of the ferromagnet. This contrasts with what one obtains in the p

NP Vaisakh, Suman Bhattacharjee, Sunita Srivastava

The interaction between macroscopic fluid flow and nanoscale forces has resulted in the formation of long-range assemblies through evaporation-induced self-assembly. Anisotropic gold nanorods (AuNR) can form disordered, smectic, or vertically ordered long-range structures, but controlling their assembly remains a challenge and requires a deeper understanding

Sheng Cheng, Maitreya Patel, Yezhou Yang

Despite advancements in text-to-image models, generating images that precisely align with textual descriptions remains challenging due to misalignment in training data. In this paper, we analyze the critical role of caption precision and recall in text-to-image model training. Our analysis of human-annotated captions shows that both precision and recall are

Hendry M. Lim, Genko T. Genov, Roberto Sailer, Alfaiz Fahrurrachman

Decoherence is a major challenge for quantum technologies. A way to mitigate its negative impact is by employing quantum optimal control. The decoherence dynamics varies significantly based on the characteristics of the surrounding environment of qubits, consequently affecting the outcome of the control optimization. In this work, we investigate the dependen

Chih-Yu Lo, Po-Yao Chang

Topological entanglement entropy (TEE) represents an intrinsic contribution to the entanglement entropy (EE) in topologically ordered systems. In quantum information theory, strong subadditivity (SSA) is a fundamental property of EE, reflecting the non-negativity of conditional mutual information. TEE was originally believed to be a universal correction to t

Bo Yang

We present here a complete microscopic theory of a family of neutral excitations in the fractional quantum Hall fluids, related to the geometric fluctuations of the quantum Hall ground states. Many of the physical properties of such geometric modes can be inferred analytically. These include the chirality, multiplicity and energy of these geometric modes, as

Andrii Dashko, Andreas Ekstedt

In this paper, we investigate the dynamics of the nucleating scalar field during the first-order phase transitions by incorporating one-loop corrections of classical fluctuations. We assume that a high-temperature expansion is valid\te where the mass of the scalar field is significantly smaller than the temperature\te so that we can treat the bubble-wall dyn

Juhi Tiwari, Ming Sun, Rongxin Luo, Matteo Fossati

Ram pressure stripping (RPS) is an important process that plays a significant role in shaping the evolution of cluster galaxies and their surrounding environment. Despite its recognized significance, the potential connection between RPS and AGN activity in cluster galaxies remains poorly understood. Recent claims, based on optical emission line diagnostics,

Giuliano Giudici, Stefano Veroni, Giacomo Giudice, Hannes Pichler

The advent of digital neutral-atom quantum computers relies on the development of fast and robust protocols for high-fidelity quantum operations. In this work, we introduce a novel scheme for entangling gates using four atomic levels per atom: a ground-state qubit and two Rydberg states. A laser field couples the qubit to one of the two Rydberg states, while

R. A. J. Eyles-Ferris, P. A. Evans, A. A. Breeveld, S. B. Cenko

The LIGO, Virgo and KAGRA gravitational wave observatories are currently undertaking their O4 observing run offering the opportunity to discover new electromagnetic counterparts to gravitational wave events. We examine the capability of the Neil Gehrels Swift Observatory (Swift) to respond to these triggers, primarily binary neutron star mergers, with both t

Gal Shavit, Jason Alicea

Coulomb repulsion can, counterintuitively, mediate Cooper pairing via the Kohn-Luttinger mechanism. However, it is commonly believed that observability of the effect requires special circumstances -- e.g., vicinity of the Fermi level to van Hove singularities, significant lattice-induced band distortions, or non-trivial Fermi surface topologies. Here we esta

Yue Zhang

Neutrino self-interaction with a larger ``Fermi constant'' is often resorted to for understanding various puzzles of our universe. We point out that a light, neutrinophilic scalar particle $\phi$ through radiative correction leads to an energy-scale dependence in the neutrino-$Z$-boson gauge coupling. The driver behind this phenomenon is a large separation b

Leah K. Morabito, R. Kondapally, P. N. Best, B. -H. Yue

Both star formation (SF) and Active Galactic Nuclei (AGN) play an important role in galaxy evolution. Statistically quantifying their relative importance can be done using radio luminosity functions. Until now these relied on galaxy classifications, where sources with a mixture of radio emission from SF and AGN are labelled as either a star-forming galaxy or

Raschid Abedin, Wenjun Niu

In a previous work, we have constructed the Yangian $Y_\hbar (\mathfrak{d})$ of the cotangent Lie algebra $\mathfrak{d}=T^*\mathfrak{g}$ for a simple Lie algebra $\mathfrak{g}$, from the geometry of the equivariant affine Grassmanian associated to $G$ with $\mathfrak{g}=\mathrm{Lie}(G)$. In this paper, we construct a quantum groupoid $\Upsilon_\hbar^\sigma (

Thomas Bartsch

Global internal symmetries act unitarily on local observables or states of a quantum system. In this note, we aim to generalise this statement to extended observables by considering unitary actions of finite global 2-group symmetries $\mathcal{G}$ on line operators. We propose that the latter transform in unitary 2-representations of $\mathcal{G}$, which we

Grant C. Weldon, Smadar Naoz, Bradley M. S. Hansen

The large eccentricities of cold Jupiters and the existence of hot Jupiters have long challenged theories of planet formation. A proposed solution to both of these puzzles is high-eccentricity migration, in which an initially cold Jupiter is excited to high eccentricities before being tidally circularized. Secular perturbations from an inclined stellar compa

Wei-Xiang Feng, Simeon Bird, Hai-Bo Yu

The origin of the binary black hole mergers observed by LIGO--Virgo--KAGRA remains an open question. We calculate the merger rate from primordial black holes (PBHs) within the density spike around supermassive black holes (SMBHs) at the centers of galaxies. We show that the merger rate within the spike is comparable to that within the wider dark matter halo.

Miquel Miravet-Tenés, Martin E. Pessah

The magnetorotational instability (MRI) is considered a leading mechanism for driving angular momentum transport in differentially rotating astrophysical flows, including accretion disks and protoneutron stars. This process is mediated by the exponential amplification of the magnetic field whose final amplitude is envisioned to be limited by secondary (paras

Alessandra Mastrobuono-Battisti, Pau Amaro Seoane, Màrius Josep Fullana i Alfonso, Chingis Omarov

We address the orbital distribution of stars in merging nuclear star clusters (NSCs) and the subsequent effects on supermassive black hole binary (SMBHB) evolution. We ran direct-summation $N$-body simulations with different initial conditions to do a detailed study of the resulting NSC after their progenitors had merged. Our findings reveal that prograde st

Csaba Balazs, Torsten Bringmann, Felix Kahlhoefer, Martin White

Dark matter is a fundamental constituent of the universe, which is needed to explain a wide variety of astrophysical and cosmological observations. Although the existence of dark matter was first postulated nearly a century ago and its abundance is precisely measured, approximately five times larger than that of ordinary matter, its underlying identity remai

Muyang Li, Yujun Lin, Zhekai Zhang, Tianle Cai

Diffusion models can effectively generate high-quality images. However, as they scale, rising memory demands and higher latency pose substantial deployment challenges. In this work, we aim to accelerate diffusion models by quantizing their weights and activations to 4 bits. At such an aggressive level, both weights and activations are highly sensitive, where

Jun-Kun Chen, Yu-Xiong Wang

This paper proposes ProEdit - a simple yet effective framework for high-quality 3D scene editing guided by diffusion distillation in a novel progressive manner. Inspired by the crucial observation that multi-view inconsistency in scene editing is rooted in the diffusion model's large feasible output space (FOS), our framework controls the size of FOS and red

Shuhong Zheng, Zhipeng Bao, Ruoyu Zhao, Martial Hebert

Beyond high-fidelity image synthesis, diffusion models have recently exhibited promising results in dense visual perception tasks. However, most existing work treats diffusion models as a standalone component for perception tasks, employing them either solely for off-the-shelf data augmentation or as mere feature extractors. In contrast to these isolated and

David Junhao Zhang, Roni Paiss, Shiran Zada, Nikhil Karnad

Recently, breakthroughs in video modeling have allowed for controllable camera trajectories in generated videos. However, these methods cannot be directly applied to user-provided videos that are not generated by a video model. In this paper, we present ReCapture, a method for generating new videos with novel camera trajectories from a single user-provided v

Ray Hagimoto, Andrew J. Long, Mustafa A. Amin

Axion-like particles may form a network of cosmic strings in the Universe today that can rotate the plane of polarization of cosmic microwave background (CMB) photons. Future CMB observations with improved sensitivity might detect this axion-string-induced birefringence effect, thereby revealing an as-yet unseen constituent of the Universe and offering a new

David M. Chan, Rodolfo Corona, Joonyong Park, Cheol Jun Cho

With the introduction of transformer-based models for vision and language tasks, such as LLaVA and Chameleon, there has been renewed interest in the discrete tokenized representation of images. These models often treat image patches as discrete tokens, analogous to words in natural language, learning joint alignments between visual and human languages. Howev

Jonathan Roberts, Kai Han, Samuel Albanie

As the context limits of Large Language Models (LLMs) increase, the range of possible applications and downstream functions broadens. In many real-world tasks, decisions depend on details scattered across collections of often disparate documents containing mostly irrelevant information. Long-context LLMs appear well-suited to this form of complex information

Peiqi Liu, Zhanqiu Guo, Mohit Warke, Soumith Chintala

Significant progress has been made in open-vocabulary mobile manipulation, where the goal is for a robot to perform tasks in any environment given a natural language description. However, most current systems assume a static environment, which limits the system's applicability in real-world scenarios where environments frequently change due to human interven

Keshigeyan Chandrasegaran, Agrim Gupta, Lea M. Hadzic, Taran Kota

We present HourVideo, a benchmark dataset for hour-long video-language understanding. Our dataset consists of a novel task suite comprising summarization, perception (recall, tracking), visual reasoning (spatial, temporal, predictive, causal, counterfactual), and navigation (room-to-room, object retrieval) tasks. HourVideo includes 500 manually curated egoce

Weiquan Huang, Aoqi Wu, Yifan Yang, Xufang Luo

CLIP is a seminal multimodal model that maps images and text into a shared representation space through contrastive learning on billions of image-caption pairs. Inspired by the rapid progress of large language models (LLMs), we investigate how the superior linguistic understanding and broad world knowledge of LLMs can further strengthen CLIP, particularly in

Weixin Liang, Lili Yu, Liang Luo, Srinivasan Iyer

The development of large language models (LLMs) has expanded to multi-modal systems capable of processing text, images, and speech within a unified framework. Training these models demands significantly larger datasets and computational resources compared to text-only LLMs. To address the scaling challenges, we introduce Mixture-of-Transformers (MoT), a spar

Andrey Boris Khesin, Jonathan Z. Lu, Peter W. Shor

While stabilizer tableaus have proven useful as a descriptive tool for additive quantum codes, they otherwise offer little guidance for concrete constructions or algorithm analysis. We introduce a representation of stabilizer codes as graphs with certain structures, and prove via the ZX Calculus that this representation is related to stabilizer tableaus by a

AmirEhsan Khorashadizadeh, Tobías I. Liaudat, Tianlin Liu, Jason D. McEwen

Neural fields or implicit neural representations (INRs) have attracted significant attention in computer vision and imaging due to their efficient coordinate-based representation of images and 3D volumes. In this work, we introduce a coordinate-based framework for solving imaging inverse problems, termed LoFi (Local Field). Unlike conventional methods for im

Nari Johnson, Elise Silva, Harrison Leon, Motahhare Eslami

Most AI tools adopted by governments are not developed internally, but instead are acquired from third-party vendors in a process called public procurement. In this paper, we conduct the first empirical study of how United States cities' procurement practices shape critical decisions surrounding public sector AI. We conduct semi-structured interviews with 19

Julio C. Magdalena de la Fuente, Tyler D. Ellison, Meng Cheng, Dominic J. Williamson

We construct a family of two-dimensional topological stabilizer codes on continuous variable (CV) degrees of freedom, which generalize homological rotor codes and the toric-GKP code. Our topological codes are built using the concept of boson condensation -- we start from a parent stabilizer code based on an $\mathbb{R}$ gauge theory and condense various boso

Kieran A. Murphy, Zhuowen Yin, Dani S. Bassett

Whether the system under study is a shoal of fish, a collection of neurons, or a set of interacting atmospheric and oceanic processes, transfer entropy measures the flow of information between time series and can detect possible causal relationships. Much like mutual information, transfer entropy is generally reported as a single value summarizing an amount

Hao Sun, Yunyi Shen, Jean-Francois Ton

The Bradley-Terry (BT) model is a common and successful practice in reward modeling for Large Language Model (LLM) alignment. However, it remains unclear why this model -- originally developed for multi-player stochastic game matching -- can be adopted to convert pairwise response comparisons to reward values and make predictions. Especially given the fact t

Nikita Karagodin, Yury Polyanskiy, Philippe Rigollet

This work presents a modification of the self-attention dynamics proposed by Geshkovski et al. (arXiv:2312.10794) to better reflect the practically relevant, causally masked attention used in transformer architectures for generative AI. This modification translates into an interacting particle system that cannot be interpreted as a mean-field gradient flow.

Koichi Namekata, Sherwin Bahmani, Ziyi Wu, Yash Kant

Methods for image-to-video generation have achieved impressive, photo-realistic quality. However, adjusting specific elements in generated videos, such as object motion or camera movement, is often a tedious process of trial and error, e.g., involving re-generating videos with different random seeds. Recent techniques address this issue by fine-tuning a pre-

Tom Hutchcroft, Isaac M. Lopez

We prove an inequality relating the isoperimetric profile of a graph to the decay of the random walk total variation distance $\sup_{x\sim y} ||P^n(x,\cdot)-P^n(y,\cdot)||_{\mathrm{TV}}$. This inequality implies a quantitative version of a theorem of Salez (GAFA 2022) stating that bounded-degree graphs of non-negative Ollivier-Ricci curvature cannot be expan

Aviv Netanyahu, Yilun Du, Antonia Bronars, Jyothish Pari

Learning the intents of an agent, defined by its goals or motion style, is often extremely challenging from just a few examples. We refer to this problem as task concept learning and present our approach, Few-Shot Task Learning through Inverse Generative Modeling (FTL-IGM), which learns new task concepts by leveraging invertible neural generative models. The

Zhaofeng Wu, Xinyan Velocity Yu, Dani Yogatama, Jiasen Lu

Modern language models can process inputs across diverse languages and modalities. We hypothesize that models acquire this capability through learning a shared representation space across heterogeneous data types (e.g., different languages and modalities), which places semantically similar inputs near one another, even if they are from different modalities/l

Yuanjie Ren, Nathanan Tantivasadakarn, Dominic J. Williamson

The classification of topological phases of matter is a fundamental challenge in quantum many-body physics, with applications to quantum technology. Recently, this classification has been extended to the setting of Adaptive Finite-Depth Local Unitary (AFDLU) circuits which allow global classical communication. In this setting, the trivial phase is the collec

Chen Gao, Yipeng Wang, Changil Kim, Jia-Bin Huang

Neural Radiance Fields (NeRF) have demonstrated exceptional capabilities in reconstructing complex scenes with high fidelity. However, NeRF's view dependency can only handle low-frequency reflections. It falls short when handling complex planar reflections, often interpreting them as erroneous scene geometries and leading to duplicated and inaccurate scene r

Gaoyue Zhou, Hengkai Pan, Yann LeCun, Lerrel Pinto

The ability to predict future outcomes given control actions is fundamental for physical reasoning. However, such predictive models, often called world models, remains challenging to learn and are typically developed for task-specific solutions with online policy learning. To unlock world models' true potential, we argue that they should 1) be trainable on o

Ivan V. Dudinets, Oleg Lychkovskiy

We study a quantum system that consists of two fermionic chains coupled by a driven quantum point contact (QPC). The QPC contains a bond with a periodically varying tunneling amplitude. Initially the left chain is packed with fermions while the right one is empty. We numerically track the evolution of the system and demonstrate that, at frequencies above a c

Dylan Manuel, Nafis Tanveer Islam, Joseph Khoury, Ana Nunez

Security experts reverse engineer (decompile) binary code to identify critical security vulnerabilities. The limited access to source code in vital systems - such as firmware, drivers, and proprietary software used in Critical Infrastructures (CI) - makes this analysis even more crucial on the binary level. Even with available source code, a semantic gap per

Morgan Choi, Christian Pluchar, Wenhua He, Saikat Guha

We explore the use of a spatial mode sorter to image a nanomechanical resonator, with the goal of studying the quantum limits of active imaging and extending the toolbox for optomechanical force sensing. In our experiment, we reflect a Gaussian laser beam from a vibrating nanoribbon and pass the reflected beam through a commercial spatial mode demultiplexer

Ashley Montanaro, Leo Zhou

We present new advances towards achieving exponential quantum speedups for solving optimization problems by low-depth quantum algorithms. Specifically, we focus on families of combinatorial optimization problems that exhibit symmetry and contain planted solutions. We rigorously prove that the 1-step Quantum Approximate Optimization Algorithm (QAOA) can achie

Nathan Constantinides, Ali Fahimniya, Dhruv Devulapalli, Dolev Bluvstein

Neutral atom arrays have emerged as a promising platform for both analog and digital quantum processing. Recently, devices capable of reconfiguring arrays during quantum processes have enabled new applications for these systems. Atom reconfiguration, or routing, is the core mechanism for programming circuits; optimizing this routing can increase processing s

Chris Akers, Adam Bouland, Lijie Chen, Tamara Kohler

The `quantum gravity in the lab' paradigm suggests that quantum computers might shed light on quantum gravity by simulating the CFT side of the AdS/CFT correspondence and mapping the results to the AdS side. This relies on the assumption that the duality map (the `dictionary') is efficient to compute. In this work, we show that the complexity of the AdS/CFT

Xinan Chen, Stefano Chessa, Ian George, Felix Leditzky

Distribution of entanglement is an essential task in quantum information processing and the realization of quantum networks. In our work, we theoretically investigate the scenario where a central source prepares an N-partite entangled state and transmits each entangled subsystem to one of N receivers through noisy quantum channels. The receivers are then abl

Usman Anwar, Ashish Pandian, Jia Wan, David Krueger

Zero-shot coordination (ZSC) is a popular setting for studying the ability of reinforcement learning (RL) agents to coordinate with novel partners. Prior ZSC formulations assume the $\textit{problem setting}$ is common knowledge: each agent knows the underlying Dec-POMDP, knows others have this knowledge, and so on ad infinitum. However, this assumption rare

Gabriele Oliaro, Zhihao Jia, Daniel Campos, Aurick Qiao

Speculative decoding is widely adopted to reduce latency in large language model (LLM) inference by leveraging smaller draft models capable of handling diverse user tasks. However, emerging AI applications, such as LLM-based agents, present unique workload characteristics: instead of diverse independent requests, agentic frameworks typically submit repetitiv

F. Setiawan, Campbell McLauchlan

We propose the X$^3$Z$^3$ Floquet code, a dynamical code with improved performance under biased noise compared to other Floquet codes. The enhanced performance is attributed to a simplified decoding problem resulting from a persistent stabiliser-product symmetry, which surprisingly exists in a code without constant stabilisers. Even if such a symmetry is all

Jonathan Cohen

Let $F$ be a non-archimedean local field of characteristic zero. If $F$ has even residual characteristic, we assume $F/\mathbb{Q}_2$ is unramified. Let $V$ be a depth zero, irreducible, nongeneric supercuspidal representation of $GSp(4, F)$. We calculate the dimensions of the spaces of Siegel-invariant vectors in $V$ of level $\mathfrak{p}^n$ for all $n\geq0

Clément L. Canonne, Robin Kothari, Ryan O'Donnell

We study quantum algorithms for verifying properties of the output probability distribution of a classical or quantum circuit, given access to the source code that generates the distribution. We consider the basic task of uniformity testing, which is to decide if the output distribution is uniform on $[d]$ or $\epsilon$-far from uniform in total variation di

Camille Thibault, Jacob-Junqi Tian, Gabrielle Peloquin-Skulski, Taylor Lynn Curtis

Misinformation is a complex societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this, we have curated the largest collection of (mis)information datasets in the literature, totaling 75. From these, we evaluated the quality of 36 datasets that consist of statements or claims, as well as the 9 datasets that con

Francesco Maltese

In this article we study generalizations of the inhomogeneous Burgers equation. First at the operator level, in the sense that we replace classical differential derivations by operators with certain properties, and then we increase the spatial dimensions of the Burgers equation, which is usually studied in one spatial dimension. This allows us, in one dimens

Andreas Ekstedt, Oliver Gould, Joonas Hirvonen, Benoit Laurent

WallGo is an open source software for the computation of the bubble wall velocity in first-order cosmological phase transitions. It also computes the energy budget available for the generation of gravitational waves. The main part of WallGo, built in Python, determines the wall velocity by solving the scalar-field(s) equation of motion, the Boltzmann equatio

Viktar Kireyeu, Vadim Voronyuk, Michael Winn, Susanne Gläßel

We study the influence of the nuclear equation-of-state (EoS) on collective observables -- the directed ($v_1$) and elliptic flow ($v_2$) of nucleons and light clusters -- in heavy-ion collisions at GeV energies using the Parton-Hadron-Quantum-Molecular Dynamics (PHQMD) approach. A novel development in this work is the inclusion of a momentum-dependent nucle

Christian Vogl, Stefan Taubenberger, Géza Csörnyei, Bruno Leibundgut

The ongoing discrepancy in the Hubble constant ($H_0$) estimates obtained through local distance ladder methods and early universe observations poses a significant challenge to the $\Lambda$CDM model, suggesting potential new physics. Type II supernovae (SNe II) offer a promising technique for determining $H_0$ in the local universe independently of the trad

Anil Kag, Huseyin Coskun, Jierun Chen, Junli Cao

Neural network architecture design requires making many crucial decisions. The common desiderata is that similar decisions, with little modifications, can be reused in a variety of tasks and applications. To satisfy that, architectures must provide promising latency and performance trade-offs, support a variety of tasks, scale efficiently with respect to the

Raphael E. Hoult, Pavel Kovtun

We explore the relationship between linear and non-linear causality in theories of dissipative relativistic fluid dynamics. While for some fluid-dynamical theories, a linearized causality analysis can be used to determine whether the full non-linear theory is causal, for others it can not. As an illustration, we study relativistic viscous magnetohydrodynamic

Hongyu Wang, Shuming Ma, Furu Wei

Recent research on the 1-bit Large Language Models (LLMs), such as BitNet b1.58, presents a promising direction for reducing the inference cost of LLMs while maintaining their performance. In this work, we introduce BitNet a4.8, enabling 4-bit activations for 1-bit LLMs. BitNet a4.8 employs a hybrid quantization and sparsification strategy to mitigate the qu

Fabian A. Portilla

During this work, it is considered a binary system of supermassive rotating black holes; first, it is employed the concept of weak field limit to develop a metric tensor g that describes the geometry of the spacetime, it introduced an approximation in which the second black hole is coupled to the system through a perturbation tensor f, consequently , it is e

Advaith V. Sethuraman, Onur Bagoren, Harikrishnan Seetharaman, Dalton Richardson

Mobile robots operating indoors must be prepared to navigate challenging scenes that contain transparent surfaces. This paper proposes a novel method for the fusion of acoustic and visual sensing modalities through implicit neural representations to enable dense reconstruction of transparent surfaces in indoor scenes. We propose a novel model that leverages

Yanjun Gao, Skatje Myers, Shan Chen, Dmitriy Dligach

Large language models (LLMs) are being explored for diagnostic decision support, yet their ability to estimate pre-test probabilities, vital for clinical decision-making, remains limited. This study evaluates two LLMs, Mistral-7B and Llama3-70B, using structured electronic health record data on three diagnosis tasks. We examined three current methods of extr

Hani Barhum, Cormac McDonnell, Tamara Amro, Ilya Simanovsky

Diamond single crystals are promising nonlinear THz sources due to their high damage threshold, transparency, and small dispersion linear dispersion over THz-NIR which enables relaxing the need for additional phase-matching engineering . However, the centrosymmetry of a diamond's lattice prohibits even-order nonlinear effects, including second harmonic gener

Vladimir Baryshevsky

Experimental observation of quasi-optical phenomenon of $\gamma$-quanta polarization plane rotation in matter with polarized proton (nuclei) is demonstrated to be possible. This effect is similar to the magneto-optic Faraday effect (Faraday optical rotation). Quasi-optical birefringence effect for $\gamma$-quanta in matter with polarized nuclei having spin $

Peter Cholak, Marianna Csornyei, Neil Lutz, Patrick Lutz

It is well known that if $A \subseteq \mathbb{R}^n$ is an analytic set of Hausdorff dimension $a$, then $\dim_H(\pi_VA)=\min\{a,k\}$ for a.e.\ $V\in G(n,k)$, where $G(n,k)$ denotes the set of all $k$-dimensional subspaces of $\mathbb{R}^n$ and $\pi_V$ is the orthogonal projection of $A$ onto $V$. In this paper we study how large the exceptional set \begin{eq

C. L. Hale, I. Heywood, M. J. Jarvis, I. H. Whittam

The MeerKAT International GHz Tiered Extragalactic Exploration Survey (MIGHTEE) is one of the large survey projects using the MeerKAT telescope, covering four fields that have a wealth of ancillary data available. We present Data Release 1 of the MIGHTEE continuum survey, releasing total intensity images and catalogues over $\sim$20 deg$^2$, across three fie

Pedro Hack, Jonas Hitter, Christian B. Mendl, Alexandru Paler

There is an increasing interest in scaling tensor network methods through belief propagation (BP), as well as increasing the accuracy of BP through tensor network methods. We develop a unification framework that takes an arbitrary graphical model with loops and provides message passing update rules and inference equations. We show that recent state-of-the-ar

Mischa Dombrowski, Hadrien Reynaud, Bernhard Kainz

Latent Video Diffusion Models can easily deceive casual observers and domain experts alike thanks to the produced image quality and temporal consistency. Beyond entertainment, this creates opportunities around safe data sharing of fully synthetic datasets, which are crucial in healthcare, as well as other domains relying on sensitive personal information. Ho

Philip Lynch, Ollie Burke

We outline a practical scheme for converting between three commonly used sets of phases to describe the trajectories of extreme mass ratio inspirals; quasi-Keplerian angles, Mino time action-angles, and Boyer-Lindquist time action-angles (as utilised by the FastEMRIWaveform package). Conversion between Boyer-Lindquist time action angles and quasi-Keplerian a

Jingwei Xu, Chenyu Wang, Zibo Zhao, Wen Liu

This paper aims to design a unified Computer-Aided Design (CAD) generation system that can easily generate CAD models based on the user's inputs in the form of textual description, images, point clouds, or even a combination of them. Towards this goal, we introduce the CAD-MLLM, the first system capable of generating parametric CAD models conditioned on the

Daniel Grier, Jackson Morris

In 2005, H{\o}yer and \v{S}palek showed that constant-depth quantum circuits augmented with multi-qubit Fanout gates are quite powerful, able to compute a wide variety of Boolean functions as well as the quantum Fourier transform. They also asked what other multi-qubit gates could rival Fanout in terms of computational power, and suggested that the quantum T

Jaemin Cho, Debanjan Mahata, Ozan Irsoy, Yujie He

Document visual question answering (DocVQA) pipelines that answer questions from documents have broad applications. Existing methods focus on handling single-page documents with multi-modal language models (MLMs), or rely on text-based retrieval-augmented generation (RAG) that uses text extraction tools such as optical character recognition (OCR). However, t

Jingkai Quan, Min-Ye Zhang, Nikita Rybin, Marios Zacharias

The electronic band structure, describing the periodic dependence of electronic quantum states on lattice momentum in reciprocal space, is a fundamental concept in solid-state physics. However, it's only well-defined for static nuclei. To account for thermodynamic effects, this concept must be generalized by introducing the temperature-dependent spectral fun

Gideon Yoffe, Nachum Dershowitz, Ariel Vishne, Barak Sober

We introduce a data-centric hypothesis-testing framework to quantify the influence of sequentially correlated literary properties--such as thematic continuity--on textual classification tasks. Our method models label sequences as stochastic processes and uses an empirical autocovariance matrix to generate surrogate labelings that preserve sequential dependen

Matteo Nerini, Hongyu Li, Bruno Clerckx

Reconfigurable Intelligent Surface (RIS) is a breakthrough technology enabling the dynamic control of the propagation environment in wireless communications through programmable surfaces. To improve the flexibility of conventional diagonal RIS (D-RIS), beyond diagonal RIS (BD-RIS) has emerged as a family of more general RIS architectures. However, D-RIS and

J. E. Bonavia, S. Chockalingam, T. Cohen

In the late 1950's, Eshelby's linear solutions for the deformation field inside an ellipsoidal inclusion and, subsequently, the infinite matrix in which it is embedded were published. The solutions' ability to capture the behavior of an orthotropically symmetric shaped inclusion made it invaluable in efforts to understand the behavior of defects within, and

Lily L. Zhao, Megan E. Bedell, David W. Hogg, Rodrigo Luger

Time-varying inhomogeneities on stellar surfaces constitute one of the largest sources of radial velocity (RV) error for planet detection and characterization. We show that stellar variations, because they manifest on coherent, rotating surfaces, give rise to changes that are complex but useably compact and coherent in the spectral domain. Methods for disent

Amir M. Vahedi, Horea T. Ilies

Optimization algorithms are pivotal in advancing various scientific and industrial fields but often encounter obstacles such as trapping in local minima, saddle points, and plateaus (flat regions), which makes the convergence to reasonable or near-optimal solutions particularly challenging. This paper presents the Steepest Perturbed Gradient Descent (SPGD),

Akihiro Hokkyo, Mizuki Sanatani, Yuuya Chiba

We provide a complete classification of the integrability and nonintegrability of the spin-1 bilinear-biquadratic model with a uniaxial anisotropic field, which includes the Heisenberg model and the Affleck-Kennedy-Lieb-Tasaki model. It is rigorously shown that, within this class, the only integrable systems are those that have been solved by the Bethe ansat

Eric Wu, Kevin Wu, James Zou

There is great interest in fine-tuning frontier large language models (LLMs) to inject new information and update existing knowledge. While commercial LLM fine-tuning APIs from providers such as OpenAI and Google promise flexible adaptation for various applications, the efficacy of fine-tuning remains unclear. In this study, we introduce FineTuneBench, an ev

Qiao Duan, Qiong Li, Christopher J. Conselice, Thomas Harvey

Galaxy mergers are a key driver of galaxy formation and evolution, including the triggering of AGN and star formation to a still unknown degree. We thus investigate the impact of galaxy mergers on star formation and AGN activity using a sample of 3,330 galaxies at $z = [4.5, 8.5]$ from eight JWST fields (CEERS, JADES GOODS-S, NEP-TDF, NGDEEP, GLASS, El-Gordo