December 2024 arXiv papers — page 130

Sondre Duna Lundemo, Flavio S. Nogueira, Asle Sudbø

Two-dimensional quantum antiferromagnets are believed to host phases of matter whose excitations are more fundamental than those of the ordered phases. When combining two such spin systems in a bilayer, strong interaction between the emergent excitations can produce phases not realized in either of its subsystems. We show that the critical fluctuations of a

B. A. Seidel, K. Dolag, R. -S. Remus, J. G. Sorce

Superclusters are the most massive structures in the universe. To what degree they are actually bound against an accelerating expansion of the background is of significant cosmological and astrophysical interest. In this study, we introduce a cross matched set of superclusters from the SLOW constrained simulations of the local (z<0.05) universe. Identifying

Marius Gerbershagen, Juan Hernandez, Mikhail Khramtsov, Maria Knysh

Within the AdS/CFT correspondence, computational complexity for reduced density matrices of holographic conformal field theories has been conjectured to be related to certain geometric observables in the dual gravity theory. We study this conjecture from both the gravity and field theory point of view. Specifically, we consider a measure of complexity associ

J. Hernández-Rueda, A. S. Sanz, R. Martínez-Herrero

Surface plasmon polaritons have received much attention over the last decades in photonics or nanotechnology due to their inherent high sensitivity to metal surface variations (e.g., presence of adsorbates or changes in the roughness). It is thus expected that they will find promising major applications in widely cross-disciplinary areas, from material scien

Akshaya Jagannadharao, Nicole Beckage, Sovan Biswas, Hilary Egan

Concerns about the environmental footprint of machine learning are increasing. While studies of energy use and emissions of ML models are a growing subfield, most ML researchers and developers still do not incorporate energy measurement as part of their work practices. While measuring energy is a crucial step towards reducing carbon footprint, it is also not

W. de Roo, S. Vegetti, D. M. Powell, S. W. Ndiritu

Magnetic fields play an important role in the evolution of galaxies and in shaping the dynamics of their inter-stellar medium. However, the formation history of magnetic fields from initial seed-fields to well-ordered systems is not clear. Favoured scenarios include a turbulent dynamo that amplifies the field, and a mean-field dynamo that organizes it. Such

Asher Berlin, Yonatan Kahn

The search for dark matter and physics beyond the Standard Model has grown to encompass a highly interdisciplinary approach. In this review, we survey recent searches for light, weakly-coupled particles - axions and dark photons - over the past decade, focusing on new experimental results and the incorporation of technologies and techniques from fields as di

Michele Papucci, Ryan Plestid

We study the modifications to decay amplitudes in heavy to heavy semileptonic decays with multiple hadrons in the final state due to intermediate heavy hadrons being off-shell or having a finite width. Combining Heavy Hadron Chiral Perturbation Theory (HH$\chi$PT) with a BCFW on-shell factorization formula, we show that these effects induce $O(1/M)$ correcti

Lucas M. Valenzuela, Rhea-Silvia Remus, Marcelo M. Miller Bertolami, Roberto H. Méndez

Even after decades of usage as an extragalactic standard candle, the universal bright end of the planetary nebula luminosity function (PNLF) still lacks a solid theoretical explanation. Until now, models have modeled planetary nebulae (PNe) from artificial stellar populations, without an underlying cosmological star formation history. We present PICS (PNe In

Silvan Fischbacher, Tomasz Kacprzak, Luca Tortorelli, Beatrice Moser

We present GalSBI, a phenomenological model of the galaxy population for cosmological applications using simulation-based inference. The model is based on analytical parametrizations of galaxy luminosity functions, morphologies and spectral energy distributions. Model constraints are derived through iterative Approximate Bayesian Computation, by comparing Hy

Carolina Arbeláez, Marcela González, Martin Hirsch, Nicolás A. Neill

We discuss an extension of the standard model with a real scalar triplet, $T$, including non-renormalizable operators (NROs) up to $d=6$. If $T$ is odd under a $Z_2$ symmetry, the neutral component of $T$ is a good candidate for the dark matter (DM) of the universe. We calculate the relic density and constraints from direct and indirect detection on such a s

Joshua N. Benabou, Malte Buschmann, Joshua W. Foster, Benjamin R. Safdi

The quantum chromodynamics (QCD) axion arises as the pseudo-Goldstone mode of a spontaneously broken abelian Peccei-Quinn (PQ) symmetry. If the scale of PQ symmetry breaking occurs below the inflationary reheat temperature and the domain wall number is unity, then there is a unique axion mass that gives the observed dark matter (DM) abundance. Computing this

Dan Stefan Eniceicu, Chitraang Murdia, Andrii Torchylo

We derive an exact convergent expression for the partition function of the $\mathcal{N}=1$ $(2,4k)$ minimal superstring theory with type 0B GSO projection in the ungapped phase by leveraging the duality between this theory and a double-scaled unitary matrix integral. Taking the $k\rightarrow\infty$ limit, we also obtain the complete partition function of $\m

Lorenzo Bianchi, Leonardo S. Cardinale, Elia de Sabbata

We initiate the study of extended excitations in the long-range O(N) model. We focus on line and surface defects and we discuss the challenges of a naive generalization of the simplest defects in the short-range model. To face these challenges we propose three alternative realizations of defects in the long-range model. The first consists in introducing an a

Luca Buoninfante, Benjamin Knorr, K. Sravan Kumar, Alessia Platania

To deepen our understanding of Quantum Gravity and its connections with black holes and cosmology, building a common language and exchanging ideas across different approaches is crucial. The Nordita Program "Quantum Gravity: from gravitational effective field theories to ultraviolet complete approaches" created a platform for extensive discussions, aimed at

Robert de Mello Koch, Minkyoo Kim, Seok Kim, Jehyun Lee

We construct infinitely many new $\frac{1}{16}$-BPS cohomologies of the 4d maximal super-Yang-Mills theory and interpret them as a black hole wrapped by dual giant graviton hairs. Since the black hole inside a dual giant feels the RR 5-form flux reduced by one unit, its microstate should essentially be an $SU(N-1)$ cohomology. However, due to the fortuitous

Hannah Seabrook, Emilien Lavie, Teodor Strömberg, Matthew P. Stafford

Quantum key distribution (QKD) offers a theoretically secure method to share secret keys, yet practical implementations face challenges due to noise and loss over long-distance channels. Traditional QKD protocols require extensive noise compensation, hindering their industrial scalability and lowering the achievable key rates. Alternative protocols encode lo

Javier M. Magan, Martin Sasieta, Brian Swingle

In this paper, we present a quantitative holographic relation between a microscopic measure of randomness and the geometric length of the wormhole in the black hole interior. To this end, we perturb an AdS black hole with Brownian semiclassical sources, implementing the continuous version of a random quantum circuit for the black hole. We use the random circ

Juan I. Espinoza-Retamal, Andrés Jordán, Rafael Brahm, Cristobal Petrovich

Essential information about the formation and evolution of planetary systems can be found in their architectures -- in particular, in stellar obliquity ($\psi$) -- as they serve as a signature of their dynamical evolution. Here, we present ESPRESSO observations of the Rossiter-Mclaughlin (RM) effect of 8 warm gas giants, revealing that independent of the ecc

Gabriel Cuomo, Sergei Dubovsky, Guzmán Hernández-Chifflet, Alexander Monin

We use effective string theory to study mesons with large spin $J$ in large $N_c$ QCD as rotating open strings. In the first part of this work, we formulate a consistent effective field theory (EFT) for open spinning strings with light quarks. Our EFT provides a consistent treatment of the endpoints' singularities that arise in the massless limit. We obtain

Ivano Basile, Luca Buoninfante, Francesco Di Filippo, Benjamin Knorr

Formulating a quantum theory of gravity lies at the heart of fundamental theoretical physics. This collection of lecture notes encompasses a selection of topics that were covered in six mini-courses at the Nordita PhD school "Towards Quantum Gravity". The scope was to provide a coherent picture, from its foundation to forefront research, emphasizing connecti

Shai M. Chester, Tobias Hansen, De-liang Zhong

We consider tree level scattering of gravitons in type IIA string theory on $AdS_4\times \mathbb{CP}^3$ to all orders in $\alpha'$, which is dual to the stress tensor correlator in $U(N)_k\times U(N)_{-k}$ ABJM theory in the planar large $N$ limit and to all orders in large $\lambda\sim N/k$. The small curvature expansion of this correlator, defined via a Bo

Kyle A. Corcoran, Scott M. Ransom, Alexandra C. Rosenthal, Megan E. DeCesar

We present timing solutions spanning nearly two decades for five redback (RB) systems found in globular clusters (GC), created using a novel technique that effectively "isolates" the pulsar. By accurately measuring the time of passage through periastron ($T_0$) at points over the timing baseline, we use a piecewise-continuous, binary model to get local solut

Kartik Narayan, Vibashan VS, Vishal M. Patel

Face parsing refers to the semantic segmentation of human faces into key facial regions such as eyes, nose, hair, etc. It serves as a prerequisite for various advanced applications, including face editing, face swapping, and facial makeup, which often require segmentation masks for classes like eyeglasses, hats, earrings, and necklaces. These infrequently oc

Jihao Liu, Zhiding Yu, Shiyi Lan, Shihao Wang

This paper presents StreamChat, a novel approach that enhances the interaction capabilities of Large Multimodal Models (LMMs) with streaming video content. In streaming interaction scenarios, existing methods rely solely on visual information available at the moment a question is posed, resulting in significant delays as the model remains unaware of subseque

Daniel Winter, Asaf Shul, Matan Cohen, Dana Berman

This paper introduces a tuning-free method for both object insertion and subject-driven generation. The task involves composing an object, given multiple views, into a scene specified by either an image or text. Existing methods struggle to fully meet the task's challenging objectives: (i) seamlessly composing the object into the scene with photorealistic po

Williard Joshua Jose, Hao Zhang

Multi-robot collaborative transportation is a critical capability that has attracted significant attention over recent years. To reliably transport a kinematically constrained payload, a team of robots must closely collaborate and coordinate their individual velocities to achieve the desired payload motion. For quadruped robots, a key challenge is caused by

Zixun Xie, Sicheng Zuo, Wenzhao Zheng, Yunpeng Zhang

Modeling the evolutions of driving scenarios is important for the evaluation and decision-making of autonomous driving systems. Most existing methods focus on one aspect of scene evolution such as map generation, motion prediction, and trajectory planning. In this paper, we propose a unified Generative Pre-training for Driving (GPD-1) model to accomplish all

Jinke Ren, Yaping Sun, Hongyang Du, Weiwen Yuan

This paper delves into the applications of generative artificial intelligence (GAI) in semantic communication (SemCom) and presents a thorough study. Three popular SemCom systems enabled by classical GAI models are first introduced, including variational autoencoders, generative adversarial networks, and diffusion models. For each system, the fundamental con

Christopher Chou, Lisa Dunlap, Koki Mashita, Krishna Mandal

With the growing adoption and capabilities of vision-language models (VLMs) comes the need for benchmarks that capture authentic user-VLM interactions. In response, we create VisionArena, a dataset of 230K real-world conversations between users and VLMs. Collected from Chatbot Arena - an open-source platform where users interact with VLMs and submit preferen

Alexander Pan, Lijie Chen, Jacob Steinhardt

Top-down transparency typically analyzes language model activations using probes with scalar or single-token outputs, limiting the range of behaviors that can be captured. To alleviate this issue, we develop a more expressive probe that can directly output natural language, performing LatentQA: the task of answering open-ended questions about activations. A

Will Gao, Dilin Wang, Yuchen Fan, Aljaz Bozic

We present a novel approach to shape editing, building on recent progress in 3D reconstruction from multi-view images. We formulate shape editing as a conditional reconstruction problem, where the model must reconstruct the input shape with the exception of a specified 3D region, in which the geometry should be generated from the conditional signal. To this

Shengze Wang, Jiefeng Li, Tianye Li, Ye Yuan

Single-image human mesh recovery is a challenging task due to the ill-posed nature of simultaneous body shape, pose, and camera estimation. Existing estimators work well on images taken from afar, but they break down as the person moves close to the camera. Moreover, current methods fail to achieve both accurate 3D pose and 2D alignment at the same time. Err

Yuxi Wei, Jingbo Wang, Yuwen Du, Dingju Wang

Generating realistic and interactive dynamics of traffic participants according to specific instruction is critical for street scene simulation. However, there is currently a lack of a comprehensive method that generates realistic dynamics of different types of participants including vehicles and pedestrians, with different kinds of interactions between them

Khalil Mrini, Hanlin Lu, Linjie Yang, Weilin Huang

Text-to-image generation has advanced rapidly, yet aligning complex textual prompts with generated visuals remains challenging, especially with intricate object relationships and fine-grained details. This paper introduces Fast Prompt Alignment (FPA), a prompt optimization framework that leverages a one-pass approach, enhancing text-to-image alignment effici

Graham Van Goffrier

Neutrinoless double-beta ($0\nu\beta\beta$) decay is an as-yet unobserved nuclear process, which stands to provide crucial insights for model-building beyond the Standard Model of particle physics. Its detection would simultaneously confirm the hypothesis that neutrinos are Majorana fermions, thus violating lepton-number conservation, and provide the first m

Huawei Lin, Yingjie Lao, Weijie Zhao

Identifying the training data samples that most influence a generated image is a critical task in understanding diffusion models (DMs), yet existing influence estimation methods are constrained to small-scale or LoRA-tuned models due to computational limitations. To address this challenge, we propose DMin (Diffusion Model influence), a scalable framework for

Ya Deng, Chikako Mese, Botong Wang

In 2001, de Oliveira, Katzarkov, and Ramachandran conjectured that the property of smooth projective varieties having big fundamental groups is stable under small deformations. This conjecture was proven by Beno\^it Claudon in 2010 for surfaces and for threefolds under suitable assumptions. In this paper, we prove this conjecture for smooth projective variet

Yutao Sun, Hangbo Bao, Wenhui Wang, Zhiliang Peng

Multimodal generative models require a unified approach to handle both discrete data (e.g., text and code) and continuous data (e.g., image, audio, video). In this work, we propose Latent Language Modeling (LatentLM), which seamlessly integrates continuous and discrete data using causal Transformers. Specifically, we employ a variational autoencoder (VAE) to

Shengze Wang, Xueting Li, Chao Liu, Matthew Chan

Recent breakthroughs in single-image 3D portrait reconstruction have enabled telepresence systems to stream 3D portrait videos from a single camera in real-time, democratizing telepresence. However, per-frame 3D reconstruction exhibits temporal inconsistency and forgets the user's appearance. On the other hand, self-reenactment methods can render coherent 3D

Luca Buoninfante, Long-Qi Shao, Anna Tokareva

The requirements of unitarity and causality lead to significant constraints on the Wilson coefficients of an EFT expansion, known as positivity bounds. Their standard derivation relies on the crucial assumption of polynomial boundedness on the growth of scattering amplitudes in the complex energy plane, which is a property satisfied by local QFTs, and by wea

Pegah Ahadian, Yunhe Feng, Karl Kosko, Richard Ferdig

Mathematics education, a crucial and basic field, significantly influences students' learning in related subjects and their future careers. Utilizing artificial intelligence to interpret and comprehend math problems in education is not yet fully explored. This is due to the scarcity of quality datasets and the intricacies of processing handwritten informatio

Yi-Chun Hung, Chen-Hsuan Hsu, Arun Bansil

High spin-Chern-number topological phases provide a promising low-dimensional platform for realizing double-helical edge states. In this letter, we show how these edge states can host a variety of phases driven by electron interaction effects, including multi-channel helical Luttinger liquid, spin density wave, superconducting phases, and a new type of $\pi$

Regina Ibragimova, Dimitrios Iliadis, Willem Waegeman

Recently, machine learning (ML) has gained popularity in the early stages of drug discovery. This trend is unsurprising given the increasing volume of relevant experimental data and the continuous improvement of ML algorithms. However, conventional models, which rely on the principle of molecular similarity, often fail to capture the complexities of chemical

Margalit L. Feuer, Morgan Thinel, Xiong Huang, Zhi-Hao Cui

In materials with one-dimensional electronic bands, electron-electron interactions can produce intriguing quantum phenomena, including spin-charge separation and charge density waves (CDW). Most of these systems, however, are non-magnetic, motivating a search for anisotropic materials where the coupling of charge and spin may affect emergent quantum states.

Yann Dijoux

The Householder's method is a root-find algorithm which is a natural extension of the methods of Newton and Halley. The current paper mostly focuses on approximating the square root of a positive real number based on these methods. The resulting algorithms can be expressed using Chebyshev polynomials. An extension to the nth root is also proposed.

Jimmy Wu, William Chong, Robert Holmberg, Aaditya Prasad

Exploiting the promise of recent advances in imitation learning for mobile manipulation will require the collection of large numbers of human-guided demonstrations. This paper proposes an open-source design for an inexpensive, robust, and flexible mobile manipulator that can support arbitrary arms, enabling a wide range of real-world household mobile manipul

Fabian Höfer, Niko A. Nikov

We prove logarithmic growth bounds on Sobolev norms of the focusing mass-critical NLS and gKdV equations on the torus, which hold almost surely under the focusing Gibbs measure with optimal mass threshold constructed by Oh, Sosoe, and Tolomeo [Invent. Math. 227 (2022), no. 3, 1323--1429]. More precisely, we will establish almost sure growth bounds for soluti

Quang-Anh N. D., Manh-Hung Ha, Thai Kim Dinh, Minh-Duc Pham

Major depressive disorder is a prevalent and serious mental health condition that negatively impacts your emotions, thoughts, actions, and overall perception of the world. It is complicated to determine whether a person is depressed due to the symptoms of depression not apparent. However, their voice can be one of the factor from which we can acknowledge sig

Vladimir Kulikov, Matan Kleiner, Inbar Huberman-Spiegelglas, Tomer Michaeli

Editing real images using a pre-trained text-to-image (T2I) diffusion/flow model often involves inverting the image into its corresponding noise map. However, inversion by itself is typically insufficient for obtaining satisfactory results, and therefore many methods additionally intervene in the sampling process. Such methods achieve improved results but ar

Zhuoheng Yang, Oleh Fedkevych, Roli Esha

Jet substructure studies at the Large Hadron Collider have been used to constrain parton distribution functions, test perturbative QCD, measure the strong-coupling constant, and probe the properties of the quark-gluon plasma. We extend these studies to lower energies at the Relativistic Heavy Ion Collider that would additionally allow us to test existing mod

Hongwei Niu, Jie Hu, Jianghang Lin, Guannan Jiang

Open-vocabulary panoptic segmentation aims to segment and classify everything in diverse scenes across an unbounded vocabulary. Existing methods typically employ two-stage or single-stage framework. The two-stage framework involves cropping the image multiple times using masks generated by a mask generator, followed by feature extraction, while the single-st

Sveva Castello, Ziyang Zheng, Camille Bonvin, Luca Amendola

We present a test of the equivalence principle on cosmological scales. This cornerstone of general relativity has been tested with high precision for standard matter, but its validity for the unknown dark matter remains a crucial open question. We construct a measurable quantity $E_P$ that acts as a null test, i.e. it deviates from unity whenever the weak eq

Thomas D. Cohen, Hyunwoo Oh, Veronica Wang

Recent work argued that the scaling of a dimensionless quantity $Q_D$ with path length is a better proxy for quantifying the scaling of the computational cost of maintaining adiabaticity than the timescale. It also conjectured that generically the scaling will be superlinear (although special cases exist in which it is linear). The quantity $Q_D$ depends onl

Chikh Bouzar, Amel Boudellal

Almost automorphy in the context of hyperfunctions is the main aim of this work. We give different equivalent definitions of almost automorphic hyperfunctions and then we study this class of hyperfunctions.

Mohit Garg, Suneel Sarswat

Continuous double auctions are commonly used to match orders at currency, stock, and commodities exchanges. A verified implementation of continuous double auctions is a useful tool for market regulators as they give rise to automated checkers that are guaranteed to detect errors in the trade logs of an existing exchange if they contain trades that violate th

Xiangxin Fang, Lev Mukhanov

Large Language Models (LLMs) have demonstrated great potential in various language processing tasks, and recent studies have explored their application in compiler optimizations. However, all these studies focus on the conventional open-source LLMs, such as Llama2, which lack enhanced reasoning mechanisms. In this study, we investigate the errors produced by

Matteo Zennaro, Giovanni Aricò, Carlos García-García, Raúl E. Angulo

Galaxy clustering and galaxy-galaxy lensing are two of the main observational probes in Stage-IV large-scale structure surveys. Unfortunately, the complicated relationship between galaxies and matter limits the exploitation of this data. Galaxy bias models -- such as the hybrid Lagrangian bias expansion -- allow describing galaxy clustering down to scales as

Stefanie Elisabeth Berkemeier

We study long time behavior of shear-thinning fluid flows in $d \geq 3$ dimensions, driven by additive stochastic forcing of trace class, with power-law indices ranging from $1$ to $ \frac{2d}{d+2}$. We particularly focus on Leray-Hopf solutions, i.e. on analytically weak solutions satisfying energy inequality. Introducing a new kind of energy related functi

M. Domokos, B. Schefler

The present paper completes the computation of the separating Noether numbers for the groups with order strictly less than $32$. Most of the results are proved for the case of a general (possibly finite) base field containing an element whose multiplicative order equals the size of the group.

Halil Mutuk

This study reexamines the spectroscopic parameters of light-flavor diquarks within the framework of quantum chromodynamics sum rules (QCDSR) using the inverse matrix method. Conventional QCDSR analyses are based on assumptions such as quark-hadron duality and continuum models, which introduce a degree of systematic uncertainty. The inverse matrix method circ

Vahid Balazadeh, Mohammadmehdi Ataei, Hyunmin Cheong, Amir Hosein Khasahmadi

Physical reasoning remains a significant challenge for Vision-Language Models (VLMs). This limitation arises from an inability to translate learned knowledge into predictions about physical behavior. Although continual fine-tuning can mitigate this issue, it is expensive for large models and impractical to perform repeatedly for every task. This necessitates

Madhu Kiran, Kartikey Vishnu, Rafael M. O. Cruz, Eric Granger

Image retrieval methods rely on metric learning to train backbone feature extraction models that can extract discriminant queries and reference (gallery) feature representations for similarity matching. Although state-of-the-art accuracy has improved considerably with the advent of deep learning (DL) models trained on large datasets, image retrieval remains

Paul-Hermann Balduf, Johannes Thürigen

A longstanding conjecture in $\phi^4_4$ theory is that primitive graphs dominate the beta function asymptotically at large loop order in the minimal-subtraction scheme. Here we investigate this issue by exploiting additional combinatorial structure coming from an extension to vectors with $O(N)$ symmetry. For the 0-dimensional case, we calculate the $N$-depe

Charlotte Dietze, Phan Thành Nam

We give a new proof of the compactness of minimizing sequences of the Sobolev inequalities in the critical case. Our approach relies on a simplified version of the concentration-compactness principle, which does not require any refinement of the Sobolev embedding theorem.

Jiahui Li, Yongchang Hao, Haoyu Xu, Xing Wang

Despite the advancements in training Large Language Models (LLMs) with alignment techniques to enhance the safety of generated content, these models remain susceptible to jailbreak, an adversarial attack method that exposes security vulnerabilities in LLMs. Notably, the Greedy Coordinate Gradient (GCG) method has demonstrated the ability to automatically gen

Fan Lu, Wei Wu, Kecheng Zheng, Shuailei Ma

Generating detailed captions comprehending text-rich visual content in images has received growing attention for Large Vision-Language Models (LVLMs). However, few studies have developed benchmarks specifically tailored for detailed captions to measure their accuracy and comprehensiveness. In this paper, we introduce a detailed caption benchmark, termed as C

Yunfei Qu, Deren Han

Image inverse problems have numerous applications, including image processing, super-resolution, and computer vision, which are important areas in image science. These application models can be seen as a three-function composite optimization problem solvable by a variety of primal dual-type methods. We propose a fair primal dual algorithmic framework that in

Andreas Toftegaard Kristensen, Alexios Balatsoukas-Stimming, Andreas Burg

Wireless sensing offers an alternative to wearables for contactless monitoring of human activity and vital signs. However, most existing systems use bistatic setups, which suffer from phase imperfections due to unsynchronized clocks. Monostatic systems overcome this issue, but are hindered by strong self-interference (SI) that require effective cancellation.

Stephen C. Wein, Timothée Goubault de Brugière, Luka Music, Pascale Senellart

We present three schemes for constructing a (2,2)-Shor-encoded 6-ring photonic resource state for fusion-based quantum computing, each relying on a different type of photon source. We benchmark these architectures by analyzing their ability to achieve the loss tolerance threshold for fusion-based quantum computation using the target resource state. More prec

Francesco Sinigaglia, Miroslava Dessauges-Zavadsky, Lucio Mayer, Pedro R. Capelo

Bridging theory and observations is a key task to understand galaxy formation and evolution. With the advent of state-of-the-art observational facilities, an accurate modelling of galaxy observables through radiative transfer simulations coupled to hydrodynamic simulations of galaxy formation must be performed. We present a novel pipeline, dubbed RTGen, base

Mintong Kang, Chejian Xu, Bo Li

Recent advancements in large audio-language models (LALMs) have enabled speech-based user interactions, significantly enhancing user experience and accelerating the deployment of LALMs in real-world applications. However, ensuring the safety of LALMs is crucial to prevent risky outputs that may raise societal concerns or violate AI regulations. Despite the i

Felipe Crasto de Lima, Roberto H. Miwa, Caio Lewenkopf, Adalberto Fazzio

We investigate the robustness of {\it virtual} topological states -- topological phases away from the Fermi energy -- against the electron-electron interaction and band filling. As a case study, we employ a realistic model to investigate the properties of vacancy-driven topological phases in transition metal dichalcogenides (TMDs) and establish a connection

Shauna Mooney, Leontine Alkema, Emily Sonneveldt, Kristin Bietsch

Monitoring family planning indicators, such as modern contraceptive prevalence rate (mCPR), is essential for family planning programming. The Family Planning Estimation Tool (FPET) uses survey data to estimate and forecast family planning indicators, including mCPR, over time. However, sole reliance on large-scale surveys, carried out on average every 3-5 ye

Jeremy Darling

We present new astrometric constraints on the stochastic gravitational wave background and construct the first astrometric Hellings-Downs curve using quasar proper motions. From quadrupolar vector spherical harmonic fits to the Gaia proper motions of 1,108,858 quasars, we obtain a frequency-integrated upper limit on the gravitational wave energy density, $h_

Fabian Paischer, Liu Yang, Linfeng Liu, Shuai Shao

In sequential recommendation, models recommend items based on user's interaction history. To this end, current models usually incorporate information such as item descriptions and user intent or preferences. User preferences are usually not explicitly given in open-source datasets, and thus need to be approximated, for example via large language models (LLMs

Feng Zhou, Ruiyang Liu, Chen Liu, Gaofeng He

Sewing patterns, the essential blueprints for fabric cutting and tailoring, act as a crucial bridge between design concepts and producible garments. However, existing uni-modal sewing pattern generation models struggle to effectively encode complex design concepts with a multi-modal nature and correlate them with vectorized sewing patterns that possess preci

Bruno Bianchi, Aakash Agrawal, Stanislas Dehaene, Emmanuel Chemla

Human readers can accurately count how many letters are in a word (e.g., 7 in ``buffalo''), remove a letter from a given position (e.g., ``bufflo'') or add a new one. The human brain of readers must have therefore learned to disentangle information related to the position of a letter and its identity. Such disentanglement is necessary for the compositional,

Imran Latif, Alex C. Newkirk, Matthew R. Carbone, Arslan Munir

The expansion of artificial intelligence (AI) applications has driven substantial investment in computational infrastructure, especially by cloud computing providers. Quantifying the energy footprint of this infrastructure requires models parameterized by the power demand of AI hardware during training. We empirically measured the instantaneous power draw of

Yashvi Sharma, Ashish A. Mahabal, Jesper Sollerman, Christoffer Fremling

Supernovae (SNe) come in various flavors and are classified into different types based on emission and absorption lines in their spectra. SN candidates are now abundant with the advent of large systematic sky surveys like the Zwicky Transient Facility (ZTF), however, the identification bottleneck lies in their spectroscopic confirmation and classification. F

Maria Loukaki

Let $p$ be a prime number and $\zeta_p$ a primitive $p$-th root of unity. Chebotarev's theorem states that every square submatrix of the $p \times p$ matrix $(\zeta_p^{ij})_{i,j=0}^{p-1}$ is non-singular. In this paper we prove the same for principal submatrices of $(\zeta_n^{ij})_{i,j=0}^{n-1}$, when $n=pr$ is the product of two distinct primes, and $p$ is

Ivan Arias-Arias, Maria Jose Dominguez Vazquez, Carlos Valcarcel Riveiro

By means of pilot experiments for the language pair German and Galician, this paper examines the concept of efficiency and intelligence in lexicography and artificial intelligence, AI. The aim of the experiments is to gain empirically and statistically based insights into the lexicographical text type,dictionary article, in the responses of ChatGPT 3.5, as w

Paul Ghanem, Ahmet Demirkaya, Tales Imbiriba, Alireza Ramezani

Learning dynamics governing physical and spatiotemporal processes is a challenging problem, especially in scenarios where states are partially measured. In this work, we tackle the problem of learning dynamics governing these systems when parts of the system's states are not measured, specifically when the dynamics generating the non-measured states are unkn

Davide Astesiano, Matteo Luca Ruggiero

We study the low-energy limit of General Relativity in the presence of stationarity and axial symmetry, coupled to dust. Specifically, we demonstrate that differences between the dynamics of General Relativity and those of Newtonian gravity persist even in the weak-field and slow-motion regime. Notably, these differences are driven by dragging terms that are

József Balogh, Anastasia Halfpap, Bernard Lidický, Cory Palmer

The minimum positive co-degree of a nonempty $r$-graph $H$, denoted by $\delta_{r-1}^+(H)$, is the largest integer $k$ such that for every $(r-1)$-set $S \subset V(H)$, if $S$ is contained in a hyperedge of $H$, then $S$ is contained in at least $k$ hyperedges of $H$. Given a family $\mathcal{F}$ of $r$-graphs, the positive co-degree Tur\'an function $\mathr

Sheila M. Perez-Garcia, Ashley Montanaro

Decoding low-density parity-check codes is critical in many current technologies, such as fifth-generation (5G) wireless networks and satellite communications. The belief propagation algorithm allows for fast decoding due to the low density of these codes. However, there is scope for improvement to this algorithm both in terms of its computational cost when

Jaesung R. Park, Jaewook J. Suh, Youngjoon Hong, Ernest K. Ryu

In deep learning, the recently introduced state space models utilize HiPPO (High-order Polynomial Projection Operators) memory units to approximate continuous-time trajectories of input functions using ordinary differential equations (ODEs), and these techniques have shown empirical success in capturing long-range dependencies in long input sequences. Howeve

Tiago Roxo, Joana C. Costa, Pedro Inácio, Hugo Proença

State-of-the-art Active Speaker Detection (ASD) approaches mainly use audio and facial features as input. However, the main hypothesis in this paper is that body dynamics is also highly correlated to "speaking" (and "listening") actions and should be particularly useful in wild conditions (e.g., surveillance settings), where face cannot be reliably accessed.

Arsalan Masoudifard, Mohammad Mowlavi Sorond, Moein Madadi, Mohammad Sabokrou

Ensuring that Software Requirements Specifications (SRS) align with higher-level organizational or national requirements is vital, particularly in regulated environments such as finance and aerospace. In these domains, maintaining consistency, adhering to regulatory frameworks, minimizing errors, and meeting critical expectations are essential for the reliab

Jingjing Zheng, Yankai Cao

In this work, we propose a novel Parameter-Efficient Fine-Tuning (PEFT) approach based on Gaussian Graphical Models (GGMs), marking the first application of GGMs to PEFT tasks, to the best of our knowledge. The proposed method utilizes the $\ell_{2,g}$-norm to effectively select critical parameters and capture global dependencies. The resulting non-convex op

Mingfei Han, Liang Ma, Kamila Zhumakhanova, Ekaterina Radionova

Vision-and-Language Navigation (VLN) suffers from the limited diversity and scale of training data, primarily constrained by the manual curation of existing simulators. To address this, we introduce RoomTour3D, a video-instruction dataset derived from web-based room tour videos that capture real-world indoor spaces and human walking demonstrations. Unlike ex

Tanguy Bosser, Souhaib Ben Taieb

Neural Marked Temporal Point Processes (MTPP) are flexible models to capture complex temporal inter-dependencies between labeled events. These models inherently learn two predictive distributions: one for the arrival times of events and another for the types of events, also known as marks. In this study, we demonstrate that learning a MTPP model can be frame

Pranava Seth, Vishal Upendran, Megha Anand, Janmejoy Sarkar

The Solar Ultraviolet Imaging Telescope(SUIT) onboard Aditya-L1 is an imager that observes the solar photosphere and chromosphere through observations in the wavelength range of 200-400 nm. A comprehensive understanding of the plasma and thermodynamic properties of chromospheric and photospheric morphological structures requires a large sample statistical st

Jay Bhambure, Hare Krishna

In this article, we propose a procedure for calculating the boundary stress tensor of a gravitational theory in asymptotic flat spacetime. As a case study, the stress tensor correctly reproduces the Brown-York charges for the Kerr blackhole i.e. mass and angular momentum. In asymptotic flat spacetime, there are asymptotic symmetries called BMS symmetries. We

Isha Chadalavada, Tianhui Huang, Jessica Staddon

Users increasingly query LLM-enabled web chatbots for help with scam defense. The Consumer Financial Protection Bureau's complaints database is a rich data source for evaluating LLM performance on user scam queries, but currently the corpus does not distinguish between scam and non-scam fraud. We developed an LLM ensemble approach to distinguishing scam and

Hang Zhao, Qile P. Chen, Yijing Barry Zhang, Gang Yang

Both encoder-only models (e.g., BERT, RoBERTa) and large language models (LLMs, e.g., Llama3) have been widely used for text classification tasks. However, there is a lack of systematic studies comparing the performance of encoder-based models and LLMs in text classification, particularly when fine-tuning is involved. This study employed a diverse range of m

Elena Berardini, Reza Dastbasteh, Josu Etxezarreta Martinez, Shreyas Jain

We propose a new systematic construction of CSS-T codes from any given CSS code using a map $\phi$. When $\phi$ is the identity map $I$, we retrieve the construction of [1] and use it to prove the existence of asymptotically good binary CSS-T codes, resolving a previously open problem in the literature, and of asymptotically good quantum LDPC CSS-T codes. We

Hao Kang, Srikant Bharadwaj, James Hensman, Tushar Krishna

Large language model (LLM) inference demands significant amount of computation and memory, especially in the key attention mechanism. While techniques, such as quantization and acceleration algorithms, like FlashAttention, have improved efficiency of the overall inference, they address different aspects of the problem: quantization focuses on weight-activati

K. V. Kaymakov, D. S. Malyshev

In this paper, we consider the minimum spanning tree problem (for short, MSTP) on an arbitrary set of $n$ points of $d$-dimensional space in $l_1$-norm. For this problem, for each fixed $d\geq 2$, there is a known algorithm of the computational complexity $O\big(n\cdot (\log\,n + \log^{r_d}\,n\cdot \log\log\,n)\ big)$, where $r_d\in \{0,1,2,4\}$ for $d\in \{

Wolfgang Gatterbauer

Finding unambiguous diagrammatic representations for first-order logical formulas and relational queries with arbitrarily nested disjunctions has been a surprisingly long-standing unsolved problem. We refer to this problem as the disjunction problem (of diagrammatic query representations). This work solves the disjunction problem. Our solution unifies, gener