November 2024 arXiv papers — page 177

Jinghan Zhang, Henry Xie, Xinhao Zhang, Kunpeng Liu

In the financial field, precise risk assessment tools are essential for decision-making. Recent studies have challenged the notion that traditional network loss functions like Mean Square Error (MSE) are adequate, especially under extreme risk conditions that can lead to significant losses during market upheavals. Transformers and Transformer-based models ar

Alberto Arletti

Most ost Big Data datasets suffer from selection bias. For example, X (Twitter) training observations differ largely from the testing offline observations as individuals on Twitter are generally more educated, democratic or left-leaning. Therefore, one major obstacle to reliable estimation is the differences between training and testing data. How can researc

Khalid Alnajjar, Mika Hämäläinen, Jack Rueter

This paper presents a methodology for training a transformer-based model to classify lexical and morphosyntactic features of Skolt Sami, an endangered Uralic language characterized by complex morphology. The goal of our approach is to create an effective system for understanding and analyzing Skolt Sami, given the limited data availability and linguistic int

Adam Ball, Y. T. Albert Law

We extend our recently identified dynamical edge mode boundary condition to $p$-form gauge theories, revealing their edge modes as Goldstone bosons arising from gauge transformations with support on the boundary. The symplectic conjugates of these edge modes correspond to the electric-field-like components normal to the boundary. We demonstrate that both the

William Kretschmer, Luowen Qian, Avishay Tal

We construct a classical oracle relative to which $\mathsf{P} = \mathsf{NP}$ but quantum-computable quantum-secure trapdoor one-way functions exist. This is a substantial strengthening of the result of Kretschmer, Qian, Sinha, and Tal (STOC 2023), which only achieved single-copy pseudorandom quantum states relative to an oracle that collapses $\mathsf{NP}$ t

Xinran Zhang, Hanqi Zhu, Yifan Duan, Wuyang Zhang

Constructing precise 3D maps is crucial for the development of future map-based systems such as self-driving and navigation. However, generating these maps in complex environments, such as multi-level parking garages or shopping malls, remains a formidable challenge. In this paper, we introduce a participatory sensing approach that delegates map-building tas

Jonas Klingwort, Vera Toepoel

When conducting a survey, many choices regarding survey design features have to be made. These choices affect the response rate of a survey. This paper analyzes the individual effects of these survey design features on the response rate. For this purpose, data from a systematic review of crime surveys conducted in Germany between 2001--2021 were used. First,

Hayeon Bang, Eunjin Choi, Megan Finch, Seungheon Doh

While piano music has become a significant area of study in Music Information Retrieval (MIR), there is a notable lack of datasets for piano solo music with text labels. To address this gap, we present PIAST (PIano dataset with Audio, Symbolic, and Text), a piano music dataset. Utilizing a piano-specific taxonomy of semantic tags, we collected 9,673 tracks f

Yi-Hsiung Hsu, Anthony Lasenby, Will Barker, Amel Durakovic

Spherically symmetric Einstein-{\ae}ther (E{\AE}) theory with a Maxwell-like kinetic term is revisited. We consider a general choice of the metric and the \ae{}ther field, finding that:~(i) there is a gauge freedom allowing one always to use a diagonal metric; and~(ii) the nature of the Maxwell equation forces the \ae{}ther field to be time-like in the coord

Daniel Kuhn, Soroosh Shafiee, Wolfram Wiesemann

Distributionally robust optimization (DRO) studies decision problems under uncertainty where the probability distribution governing the uncertain problem parameters is itself uncertain. A key component of any DRO model is its ambiguity set, that is, a family of probability distributions consistent with any available structural or statistical information. DRO

Konstantinos Mersinas, Aimee Liu, Niki Panteli

Cybercriminal profiling and cyber-attack attribution have been elusive goals world-wide, due to their effects on societal and geopolitical balance and stability. Attributing actions to a group or state is a complex endeavour, with traditional established approaches including cyber threat intelligence and analysis of technical means such as malware analysis,

Martin Scotti

In this short survey we concern ourselves with minimal codes, a classical object in coding theory. We will explain the relation between minimal codes and various other mathematical domains, in particular with finite projective geometry. This latter connection has sparked a renewed interest in minimal codes, giving rise to new constructions as well as new que

Joey Wilson, Marcelino Almeida, Min Sun, Sachit Mahajan

In this paper, we present a novel algorithm for probabilistically updating and rasterizing semantic maps within 3D Gaussian Splatting (3D-GS). Although previous methods have introduced algorithms which learn to rasterize features in 3D-GS for enhanced scene understanding, 3D-GS can fail without warning which presents a challenge for safety-critical robotic a

Jeff Murugan, Zayd Pandit, Hendrik J. R. van Zyl

We explore the relationship between complexity and duality in quantum systems, focusing on how local and non-local operators evolve under time evolution. We find that non-local operators, which are dual to local operators under specific mappings, exhibit behavior that mimics the growth of their local counterparts, particularly when considering state complexi

Maitreya Patel, Abhiram Kusumba, Sheng Cheng, Changhoon Kim

Contrastive Language-Image Pretraining (CLIP) models maximize the mutual information between text and visual modalities to learn representations. This makes the nature of the training data a significant factor in the efficacy of CLIP for downstream tasks. However, the lack of compositional diversity in contemporary image-text datasets limits the compositiona

Kazusato Oko, Yujin Song, Taiji Suzuki, Denny Wu

Transformers can efficiently learn in-context from example demonstrations. Most existing theoretical analyses studied the in-context learning (ICL) ability of transformers for linear function classes, where it is typically shown that the minimizer of the pretraining loss implements one gradient descent step on the least squares objective. However, this simpl

Paul L. Ebert, Paul Kühler, Dominik Stöckinger, Matthias Weißwange

The mathematical consistency of the BMHV scheme of dimensional regularisation (DReg) comes at the cost of requiring symmetry-restoring counterterms to cancel the regularisation-induced breaking of gauge and BRST invariance. There is no unique way to extend a 4-dimensional theory to $D$ dimensions, and different choices can be made for the dimensionally regul

Xiwen Chen, Sayed Pedram Haeri Boroujeni, Xin Shu, Huayu Li

Despite recent progress in reducing road fatalities, the persistently high rate of traffic-related deaths highlights the necessity for improved safety interventions. Leveraging large-scale graph-based nationwide road network data across 49 states in the USA, our study first posits the Concurrency Hypothesis from intuitive observations, suggesting a significa

Mateusz Cedro, David Martens

Graph Neural Networks (GNNs) are a powerful technique for machine learning on graph-structured data, yet they pose challenges in interpretability. Existing GNN explanation methods usually yield technical outputs, such as subgraphs and feature importance scores, that are difficult for non-data scientists to understand and thereby violate the purpose of explan

Diyi Liu, Yangsong Gu, Lee D. Han

In transportation, Weigh-in motion (WIM) stations, Electronic Toll Collection (ETC) systems, Closed-circuit Television (CCTV) are widely deployed to collect data at different locations. Vehicle re-identification, by matching the same vehicle at different locations, is helpful in understanding the long-distance journey patterns. In this paper, the potential h

Sshubam Verma, Mohammed Safi Ur Rahman Khan, Vishwajeet Kumar, Rudra Murthy

Evaluating Large Language Models (LLMs) in low-resource and linguistically diverse languages remains a significant challenge in NLP, particularly for languages using non-Latin scripts like those spoken in India. Existing benchmarks predominantly focus on English, leaving substantial gaps in assessing LLM capabilities in these languages. We introduce MILU, a

Edward Vendrow, Omiros Pantazis, Alexander Shepard, Gabriel Brostow

We introduce INQUIRE, a text-to-image retrieval benchmark designed to challenge multimodal vision-language models on expert-level queries. INQUIRE includes iNaturalist 2024 (iNat24), a new dataset of five million natural world images, along with 250 expert-level retrieval queries. These queries are paired with all relevant images comprehensively labeled with

Mowafak Allaham, Kimon Kieslich, Nicholas Diakopoulos

Expert-driven frameworks for impact assessments (IAs) may inadvertently overlook the effects of AI technologies on the public's social behavior, policy, and the cultural and geographical contexts shaping the perception of AI and the impacts around its use. This research explores the potentials of fine-tuning LLMs on negative impacts of AI reported in a diver

Jon Nelson, Joel Rajakumar, Dominik Hangleiter, Michael J. Gullans

We construct a polynomial-time classical algorithm that samples from the output distribution of noisy geometrically local Clifford circuits with any product-state input and single-qubit measurements in any basis. Our results apply to circuits with nearest-neighbor gates on an $O(1)$-D architecture with depolarizing noise after each gate. Importantly, we assu

Gabriela Barenboim, Stephen J. Parke

The interference between the atmospheric and solar neutrino oscillation sub-amplitudes is said to be responsible for CP violation (CPV) in neutrino appearance channels. More precisely, CPV is generated by the interference between the parts of the neutrino oscillation amplitude which are CP even and CP odd: even or odd when the neutrino mixing matrix is repla

Roberto Casarin, Antonio Peruzzi

The techniques suggested in Fr\"uhwirth-Schnatter et al. (2024) concern sparsity and factor selection and have enormous potential beyond standard factor analysis applications. We show how these techniques can be applied to Latent Space (LS) models for network data. These models suffer from well-known identification issues of the latent factors due to likelih

Matthew S. Clement, Elisa V. Quintana, Kevin B. Stevenson

The discovery of seven ~Earth-mass planets, orbiting the 0.09 solar mass M-Dwarf TRAPPIST-1 captivated the public and sparked a proliferation of investigations into the system's origins. Among other properties, the resonant architecture of the planets has been interpreted to imply that orbital migration played a dominant role in the system's early formation.

Lindia Tjuatja, Graham Neubig, Tal Linzen, Sophie Hao

When comparing the linguistic capabilities of language models (LMs) with humans using LM probabilities, factors such as the length of the sequence and the unigram frequency of lexical items have a significant effect on LM probabilities in ways that humans are largely robust to. Prior works in comparing LM and human acceptability judgments treat these effects

S. Purkayastha, N. Kanekar, S. Kumari, J. Rhoads

We have used the Karl G. Jansky Very Large Array (VLA) to map H{\sc i} 21\,cm emission from the Green Pea galaxy GP~J1148+2546 at $z\approx0.0451$, only the second measurement of the H{\sc i} spatial distribution of a Green Pea. The VLA H{\sc i} 21\,cm image, the DECaLS optical image, and Sloan Digital Sky Survey spectroscopy show that GP~J1148+2546 has two

Gabriel Cardoso, Hsiu-Chung Yeh, Leonid Korneev, Alexander G. Abanov

Symmetry-protected topological (SPT) phases in insulators and superconductors are known for their robust edge modes, linked to bulk invariants through the bulk-boundary correspondence. While this principle traditionally applies to gapped phases, recent advances have extended it to gapless systems, where topological edge states persist even in the absence of

Sai Krishna Ghanta, Ramviyas Parasuraman

In indoor environments, multi-robot visual (RGB-D) mapping and exploration hold immense potential for application in domains such as domestic service and logistics, where deploying multiple robots in the same environment can significantly enhance efficiency. However, there are two primary challenges: (1) the "ghosting trail" effect, which occurs due to overl

Mauro E. S. Morales, Lirandë Pira, Philipp Schleich, Kelvin Koor

Solving linear systems of equations plays a fundamental role in numerous computational problems from different fields of science. The widespread use of numerical methods to solve these systems motivates investigating the feasibility of solving linear systems problems using quantum computers. In this work, we provide a survey of the main advances in quantum l

Joseph M. Akana Murphy, Rafael Luque, Natalie M. Batalha

We conduct experiments on both real and synthetic radial velocity (RV) data to quantify the impact that observing cadence, the number of RV observations, and undetected companions all have on the accuracy of small planet mass measurements. We run resampling experiments on four systems with small transiting planets and substantial public data from HIRES in or

Dan Pirjol, Xiaoyu Wang, Lingjiong Zhu

We derive the short-maturity asymptotics for prices of options on realized variance in local-stochastic volatility models. We consider separately the short-maturity asymptotics for out-of-the-money and in-the-money options cases. The analysis for the out-of-the-money case uses large deviations theory and the solution for the rate function involves solving a

Roberto Ruiz, Alejandro Sopena, Esperanza López, Germán Sierra

The Bethe Ansatz is a method for constructing exact eigenstates of quantum-integrable spin chains. Recently, deterministic quantum algorithms, referred to as "algebraic Bethe circuits", have been developed to prepare Bethe states for the spin-1/2 XXZ model. These circuits represent a unitary formulation of the standard algebraic Bethe Ansatz, expressed using

Joseph M. Akana Murphy, Rafael Luque, Natalie M. Batalha, Ilaria Carleo

We present a revised mass measurement for HD 119130 b (aka K2-292 b), a transiting planet ($P = 17$ days, $R_\mathrm{p} = 2.63^{+0.11}_{-0.10}$ $R_\mathrm{\oplus}$) orbiting a chromospherically inactive G dwarf, previously thought to be one of the densest sub-Neptunes known. Our follow-up Doppler observations with HARPS, HARPS-N, and HIRES reveal that HD 119

Monika Kudlinska

We define a new notion of splitting complexity for a group $G$ along a non-trivial integral character $\phi \in H^1(G; \mathbb{Z})$. If $G$ is a one-ended coherent right-angled Artin group, we show that the splitting complexity along an epimorphism $\phi \colon G \to \mathbb{Z}$ equals the $L^2$-Euler characteristic of the kernel of $\phi$. This allows us to

Prasun Dhang, Jason Dexter, Mitchell C. Begelman

The presence of a strong, large-scale magnetic field in an accretion flow leads to extraction of the rotational energy of the black hole (BH) through the Blandford-Znajek (BZ) process, believed to power relativistic jets in various astrophysical sources. We study rotational energy extraction from a BH surrounded by a highly magnetized thin disk by performing

Yuxi Lu, Cecilia Mateu, K. Z. Stanek

RR Lyrae stars (RRLs) are easy to identify thanks to their large photometric variation and short periods. All stars in the RRL instability strip are pulsators is often a hidden assumption in most stellar population studies using RRLs. Non-variable stars in the instability strip have been discovered for Cepheids and $\delta$ Scuti, and in this paper, we repor

Hemant Prajapati, Rahul Srivastava

We introduce a new class of $U(1)_X$ symmetries where all Standard Model fermions are ``chiral," i.e., the left- and right-handed components have different charges under the $U(1)_X$ symmetry. Gauge anomaly cancellation is achieved by introducing three Standard Model gauge singlet dark fermions ($f^i$; $i=1,2,3$) charged under this symmetry. We systematicall

João P. A. Novo

In Newtonian gravity, it is well known that Kepler's problem admits no bound solutions in more than three spatial dimensions. This limitation extends naturally to General Relativity, where Tangherlini demonstrated that Schwarzschild black holes in higher dimensions admit no bound timelike geodesics. However, an analogous result for the rotating counterpart o

R. Sekhar Chivukula, Joshua A. Gill, Kirtimaan A. Mohan, George Sanamyan

We revisit the phenomenology of dark-matter (DM) scenarios within radius-stabilized Randall-Sundrum models. Specifically, we consider models where the dark matter candidates are Standard Model (SM) singlets confined to the TeV brane and interact with the SM via spin-2 and spin-0 gravitational Kaluza-Klein (KK) modes. We compute the thermal relic density of D

F. Muratore, A. P. Milone, F. D'Antona, E. J. Nastasio

Nearly all star clusters younger than ~600 Myr exhibit extended main sequence turn offs and split main sequences (MSs) in their color-magnitude diagrams. Works based on both photometry and spectroscopy have firmly demonstrated that the red MS is composed of fast-rotating stars, whereas blue MS stars are slow rotators. Nevertheless, the mechanism responsible

Andriy Smolyanyuk, Libor Šmejkal, Igor I. Mazin

RuO$_2$ is one of the most highlighted candidates for altermagnetism. However, the most recent muon spin spectroscopy and neutron studies demonstrated the absence of magnetic order in this system. The electronic structure of RuO$_2$ hints at a possibility of realizing a magnetically ordered state upon hole doping, and such a possibility was explored experime

Uri Keshet

Radio-to-$\gamma$-ray signals, recently found narrowly confined near the characteristic $2.4R_{500}$ scaled radii of galaxy clusters and groups, have been associated with their virial (structure-formation accretion) shocks based on spectro-spatial characteristics. By stacking high-latitude GMIMS radio data around MCXC galaxy clusters, we identify ($3\sigma$-

Tim Chan

This note proposes a simpler method to extract the logical error rate from an emulated surface code memory experiment.

Tomasz R. Taylor, Bin Zhu

We develop a formalism for computing the scattering amplitudes in maximally symmetric de Sitter spacetime with compact spatial dimensions. We describe quantum states by using the representation theory of de Sitter symmetry group and link the Hilbert space to geodesic observers. The positive and negative ``energy'' wavefunctions are uniquely determined by the

Hui Yang, Ya-Hui Zhang

We performed a random phase approximation (RPA) calculation for a spin-valley polarized model of the rhombohedral tetra-layer graphene to study the possibility of chiral superconductor from the Kohn-Luttinger mechanism. We included the realistic band structure and form factor in our calculation and solved the self-consistent equation numerically by sampling

F. Calura, R. Pascale, O. Agertz, E. Andersson

Star clusters stand at the crossroads between galaxies and single stars. Resolving the formation of star clusters in cosmological simulations represents an ambitious and challenging goal, since modelling their internal properties requires very high resolution. This paper is the third of a series within the SImulating the Environment where Globular clusters E

Yoo Jung Kim, Michael P. Fitzgerald, Jonathan Lin, Julien Lozi

Spectroastrometry, which measures wavelength-dependent shifts in the center of light, is well-suited for studying objects whose morphology changes with wavelength at very high angular resolutions. Photonic lantern (PL)-fed spectrometers have potential to enable measurement of spectroastrometric signals because the relative intensities between the PL output S

Mainak Pal, Madhumita Sarkar, K. Sengupta, Arnab Sen

We demonstrate that the kinematically-constrained model of Rydberg atoms on a two-leg ladder with staggered detuning, $\Delta \in [0,1]$, has quantum many-body scars (QMBS) in its spectrum and represents a non-perturbative generalization of the paradigmatic PXP model defined on a chain. We show that these QMBS result in coherent many-body revivals and site-d

Federico Lelli, Tobias Mistele, Stacy S. McGaugh, James M. Schombert

Galaxies obey a set of strict dynamical laws, which imply a close coupling between the visible matter (stars and gas) and the observed dynamics (set by dark matter in the standard cosmological context). Here we review recent results from weak gravitational lensing, which allows studying the empirical laws of galaxy dynamics out to exceedingly large radii in

Alberto Salvio

This review article provides the basics and discusses some important applications of thermal field theory, namely the combination of statistical mechanics and relativistic quantum field theory. In a first part the fundamentals are covered: the density matrix, the corresponding averages and the treatment of fields of various spin in a medium. A second part is

Jennifer E. Andrews, Manisha Shrestha, K. Azalee Bostroem, Yize Dong

We present a comprehensive multi-epoch photometric and spectroscopic study of SN 2024bch, a nearby (19.9 Mpc) Type II supernova (SN) with prominent early high ionization emission lines. Optical spectra from 2.9 days after the estimated explosion reveal narrow lines of H I, He II, C IV, and N IV that disappear by day 6. High cadence photometry from the ground

Yurii Kvasiuk, Jordan Krywonos, Matthew C. Johnson, Moritz Münchmeyer

We develop a hybrid GNN-CNN architecture for the reconstruction of 3-dimensional continuous cosmological matter fields from discrete point clouds, provided by observed galaxy catalogs. Using the CAMELS hydrodynamical cosmological simulations we demonstrate that the proposed architecture allows for an accurate reconstruction of both the dark matter and electr

Anja Butter, Sascha Diefenbacher, Nathan Huetsch, Vinicius Mikuni

Machine learning enables unbinned, highly-differential cross section measurements. A recent idea uses generative models to morph a starting simulation into the unfolded data. We show how to extend two morphing techniques, Schr\"odinger Bridges and Direct Diffusion, in order to ensure that the models learn the correct conditional probabilities. This brings di

Soumendra Kishore Roy, Lieke A. C. van Son, Anarya Ray, Will M. Farr

Next-generation gravitational wave detectors are expected to detect millions of compact binary mergers across cosmological distances. The features of the mass distribution of these mergers, combined with gravitational wave distance measurements, will enable precise cosmological inferences, even without the need for electromagnetic counterparts. However, achi

Aravind P. Ravi, Stefano Valenti, Yize Dong, Daichi Hiramatsu

We present supernova (SN) 2023ufx, a unique Type IIP SN with the shortest known plateau duration ($t_\mathrm{PT}$ $\sim$47 days), a luminous V-band peak ($M_{V}$ = $-$18.42 $\pm$ 0.08 mag), and a rapid early decline rate ($s1$ = 3.47 $\pm$ 0.09 mag (50 days)$^{-1}$). By comparing observed photometry to a hydrodynamic MESA+STELLA model grid, we constrain the

Pierluca Carenza, Maurizio Giannotti, Jordi Isern, Alessandro Mirizzi

Stars have been recognized as optimal laboratories to probe axion properties. In the last decades there have been significant advances in this field due to a better modelling of stellar systems and accurate observational data. In this work we review the current status of constraints on axions from stellar physics. We focus in particular on the Sun, globular

Mohammad Akhond, Guillermo Arias-Tamargo, Federico Carta, Julius F. Grimminger

We study Higgs branches of field theories with 8 supercharges in 5 and 6 dimensions, focusing on theories realised on 5-brane webs in Type IIB with an O$7^+$ plane, or a D6-D8-NS5 brane system in Type IIA in the presence of an O$8^+$ plane. We find magnetic quivers for the Higgs branches of these theories. The main consequence of the presence of the orientif

Andrea Cozzumbo, Ulyana Dupletsa, Rodrigo Calderón, Riccardo Murgia

Multi-messenger (MM) observations of binary neutron star (BNS) mergers provide a promising approach to trace the distance-redshift relation, crucial for understanding the expansion history of the Universe and, consequently, testing the nature of Dark Energy (DE). While the gravitational wave (GW) signal offers a direct measure of the distance to the source,

Lucas V. Pupim, Mathias S. Scheurer

We study theoretically how superlattices based on adatoms on surfaces of unconventional superconductors can be used to engineer novel pairing states that break time-reversal symmetry and exhibit non-trivial magnetic point symmetries. We illustrate this using a square-lattice Hubbard model with $d$-wave superconductivity and a subleading $s$-wave state as an

Nico Winkel, Vardha N. Bennert, Raymond P. Remigio, Tommaso Treu

The origin of the tight scaling relation between the mass of supermassive black holes (SMBHs; $M_{\rm BH}$) and their host-galaxy properties remains unclear. Active galactic nuclei (AGNs) probe phases of ongoing SMBH growth and offer the only opportunity to measure $M_{\rm BH}$ beyond the local Universe. However, determining AGN host galaxy stellar velocity

M. Libralato, L. R. Bedin, M. Griggio, D. Massari

The instruments at the focus of the Euclid space observatory offer superb, diffraction-limited imaging over an unprecedented (from space) wide field of view of 0.57 deg$^2$. This exquisite image quality has the potential to produce high-precision astrometry for point sources once the undersampling of Euclid's cameras is taken into account by means of accurat

Nikita A. Zemlevskiy

Simulations of collisions of fundamental particles on a quantum computer are expected to have an exponential advantage over classical methods and promise to enhance searches for new physics. Furthermore, scattering in scalar field theory has been shown to be BQP-complete, making it a representative problem for which quantum computation is efficient. As a ste

Admir Greljo, Hector Tiblom, Alessandro Valenti

Leveraging recent advancements in machine learning-based flavor tagging, we develop an optimal analysis for measuring the hadronic cross-section ratios $R_b$, $R_c$, and $R_s$ at the FCC-ee during its $WW$, $Zh$, and $t\bar{t}$ runs. Our results indicate up to a two-order-of-magnitude improvement in precision, providing an unprecedented test of the SM. Using

L. A. C. van Son, S. K. Roy, I. Mandel, W. M. Farr

Both detailed and rapid population studies alike predict that binary black hole (BHBH) formation is orders of magnitude more efficient at low metallicity than high metallicity, while binary neutron star (NSNS) formation remains mostly flat with metallicity, and black hole-neutron star (BHNS) mergers show intermediate behavior. This finding is a key input to

Gauthier Durieux, Grant N. Remmen, Nicholas L. Rodd, O. J. P. Éboli

In this note, we give a definitive basis for the dimension-eight operators leading to quartic -- but no cubic -- interactions among electroweak gauge bosons. These are often called anomalous quartic gauge couplings, or aQGCs. We distinguish in particular the CP-even ones from their CP-odd counterparts.

Hoang H Nguyen, Khyati Mahajan, Vikas Yadav, Julian Salazar

Although multilingual LLMs have achieved remarkable performance across benchmarks, we find they continue to underperform on non-Latin script languages across contemporary LLM families. This discrepancy arises from the fact that LLMs are pretrained with orthographic scripts, which are dominated by Latin characters that obscure their shared phonology with non-

Kumara Kahatapitiya, Haozhe Liu, Sen He, Ding Liu

Generating temporally-consistent high-fidelity videos can be computationally expensive, especially over longer temporal spans. More-recent Diffusion Transformers (DiTs) -- despite making significant headway in this context -- have only heightened such challenges as they rely on larger models and heavier attention mechanisms, resulting in slower inference spe

Jeroen M. Goedhart, Mark A. van de Wiel, Wessel N. van Wieringen, Thomas Klausch

Cancer prognosis is often based on a set of omics covariates and a set of established clinical covariates such as age and tumor stage. Combining these two sets poses challenges. First, dimension difference: clinical covariates should be favored because they are low-dimensional and usually have stronger prognostic ability than high-dimensional omics covariate

Eric Zhu, Mara Levy, Matthew Gwilliam, Abhinav Shrivastava

Training a policy that can generalize to unknown objects is a long standing challenge within the field of robotics. The performance of a policy often drops significantly in situations where an object in the scene was not seen during training. To solve this problem, we present NeRF-Aug, a novel method that is capable of teaching a policy to interact with obje

Anthony Chen, Jianjin Xu, Wenzhao Zheng, Gaole Dai

Diffusion models have demonstrated excellent capabilities in text-to-image generation. Their semantic understanding (i.e., prompt following) ability has also been greatly improved with large language models (e.g., T5, Llama). However, existing models cannot perfectly handle long and complex text prompts, especially when the text prompts contain various objec

Hao-Yu Hsu, Zhi-Hao Lin, Albert Zhai, Hongchi Xia

Modern visual effects (VFX) software has made it possible for skilled artists to create imagery of virtually anything. However, the creation process remains laborious, complex, and largely inaccessible to everyday users. In this work, we present AutoVFX, a framework that automatically creates realistic and dynamic VFX videos from a single video and natural l

Shivam Duggal, Phillip Isola, Antonio Torralba, William T. Freeman

Current vision systems typically assign fixed-length representations to images, regardless of the information content. This contrasts with human intelligence - and even large language models - which allocate varying representational capacities based on entropy, context and familiarity. Inspired by this, we propose an approach to learn variable-length token r

Satyadev Nandakumar, Subin Pulari, Akhil S, Suronjona Sarma

This paper demonstrates a duality between the non-robustness of polynomial time dimension and the existence of one-way functions. Polynomial-time dimension (denoted $\mathrm{cdim}_\mathrm{P}$) quantifies the density of information of infinite sequences using polynomial time betting algorithms called $s$-gales. An alternate quantification of the notion of pol

Yanzhe Zhang, Tao Yu, Diyi Yang

Autonomous agents powered by large vision and language models (VLM) have demonstrated significant potential in completing daily computer tasks, such as browsing the web to book travel and operating desktop software, which requires agents to understand these interfaces. Despite such visual inputs becoming more integrated into agentic applications, what types

Soohyun Park

We find decompositions of $h$-polynomials of flag doubly Cohen-Macaulay simplicial complex that yield a direct connection between gamma vectors of flag spheres and constructions used to build them geometrically. More specifically, they are determined by iterated double suspensions and a "net nonnegative set of edge subdivisions" taking it to the given flag d

Jiyu Chen, Philipp Werner

Multidimensional coherent spectroscopy (MDCS) has been established in quantum chemistry as a powerful tool for studying the nonlinear response and nonequilibrium dynamics of molecular systems. More recently, the technique has also been applied to correlated electron materials, where the interplay of localized and itinerant states makes the interpretation of

Hafsa Chaudhry, Hetvi Patel, Sai Teja Avadhootha, Sushanthik Reddy Poreddy

Gender differences were examined in sensitivity to sleep quality, in the context of blue light exposure from smartphones. Our hypothesis was created based on our journal article findings that females are more prone to be inclined to the prolonged usage of smartphones at bedtime and thus had less quality of sleep than males. The theory that usage affects slee

Guangxuan Xu, Kai Xu, Shivchander Sudalairaj, Hao Wang

Preference tuning relies on high-quality human preference data, which is often expensive and time-consuming to gather. In this paper, we introduce Dr.SoW (Density Ratio of Strong over Weak) a cost-effective method that eliminates the reliance for human annotation by leveraging off-the-shelf LLMs for preference data annotation. Dr.SoW uses the log-density rat

Jacob Kryczka, Artan Sheshmani

This is the second in a series of two papers developing a moduli-theoretic framework for differential ideal sheaves associated with formally integrable, involutive systems of algebraic partial differential equations (PDEs). Building on earlier work, which established the existence of moduli stacks for such systems with prescribed regularity and stability con

Clotilde Bizière, Wojciech Czerwiński

We consider the model of one-dimensional Pushdown Vector Addition Systems (1-PVAS), a fundamental computational model simulating both recursive and concurrent behaviours. Our main result is decidability of the reachability problem for 1-PVAS, an important open problem investigated for at least a decade. In the algorithm we actually consider an equivalent mod

Bingyi Kang, Yang Yue, Rui Lu, Zhijie Lin

OpenAI's Sora highlights the potential of video generation for developing world models that adhere to fundamental physical laws. However, the ability of video generation models to discover such laws purely from visual data without human priors can be questioned. A world model learning the true law should give predictions robust to nuances and correctly extra

Wojciech De Roeck, Vedika Khemani, Yaodong Li, Nicholas O'Dea

We generalize the proof of stability of topological order, due to Bravyi, Hastings and Michalakis, to stabilizer Hamiltonians corresponding to low-density parity check (LDPC) codes without the restriction of geometric locality in Euclidean space. We consider Hamiltonians $H_0$ defined by $[[N,K,d]]$ LDPC codes which obey certain topological quantum order con

Daiki Yamaguchi

We study the functional renormalization group equation and its solutions of the gravity having the background matters. From the system equivalence eliminating vacuum divergence, we are confirmed to give Newton coupling. We also give the path integral partition function technique to normalize setups of quantum corrected actions down to Einstein systems. Brief

Zirui Yan, Ali Tajer

Designing causal bandit algorithms depends on two central categories of assumptions: (i) the extent of information about the underlying causal graphs and (ii) the extent of information about interventional statistical models. There have been extensive recent advances in dispensing with assumptions on either category. These include assuming known graphs but u

Guangzhi Xiong, Eric Xie, Amir Hassan Shariatmadari, Sikun Guo

Large language models (LLMs) have demonstrated remarkable capabilities in various scientific domains, from natural language processing to complex problem-solving tasks. Their ability to understand and generate human-like text has opened up new possibilities for advancing scientific research, enabling tasks such as data analysis, literature review, and even e

Ramneet Kaur, Colin Samplawski, Adam D. Cobb, Anirban Roy

In this paper, we present a dynamic semantic clustering approach inspired by the Chinese Restaurant Process, aimed at addressing uncertainty in the inference of Large Language Models (LLMs). We quantify uncertainty of an LLM on a given query by calculating entropy of the generated semantic clusters. Further, we propose leveraging the (negative) likelihood of

William Martin, Daniel J. Mortlock

Model mis-specification (e.g. the presence of outliers) is commonly encountered in astronomical analyses, often requiring the use of ad hoc algorithms which are sensitive to arbitrary thresholds (e.g. sigma-clipping). For any given dataset, the optimal approach will be to develop a bespoke statistical model of the data generation and measurement processes, b

Deepika Gill, Sangeeta Sharma, Sam Shallcross

Well established for the visible spectrum gaps of the transition metal dichalcogenide family, valleytronics - the control of valley charge and current by light - is comparatively unexplored for the THz gaps that characterize graphene and topological insulators. Here we show that few cycle pulses of THz light can create and control a 100% valley polarized cur

Vade Shah, Bryce L. Ferguson, Jason R. Marden

Two-sided matching markets, environments in which two disjoint groups of agents seek to partner with one another, arise in several contexts. In static, centralized markets where agents know their preferences, standard algorithms can yield a stable matching. However, in dynamic, decentralized markets where agents must learn their preferences through interacti

Jeff J. Andrews, Simone S. Bavera, Max Briel, Abhishek Chattaraj

Whether considering rare astrophysical events on cosmological scales or unresolved stellar populations, accurate models must account for the integrated contribution from the entire history of star formation upon which that population is built. Here, we describe the second version of POSYDON, an open-source binary population synthesis code based on extensive

Kentaro Hoshisashi, Carolyn E. Phelan, Paolo Barucca

Calibrating the time-dependent Implied Volatility Surface (IVS) using sparse market data is an essential challenge in computational finance, particularly for real-time applications. This task requires not only fitting market data but also satisfying a specified partial differential equation (PDE) and no-arbitrage conditions modelled by differential inequalit

Alrick Green, Ayesha Nasim, Jaydeep Radadia, Devi Manaswi Kallam

In this study, we seek to understand how macroeconomic factors such as GDP, inflation, Unemployment Insurance, and S&P 500 index; as well as microeconomic factors such as health, race, and educational attainment impacted the unemployment rate for about 20 years in the United States. Our research question is to identify which factor(s) contributed the most to

Andrew I. Masker, Ke Zhou, Joseph P. Molnar, Samuel J. Grauer

Neural optical flow (NOF) offers improved accuracy and robustness over existing OF methods for particle image velocimetry (PIV). Unlike other OF techniques, which rely on discrete displacement fields, NOF parameterizes the physical velocity field using a continuous neural-implicit representation. This formulation enables efficient data assimilation and ensur

Neel Dey, Benjamin Billot, Hallee E. Wong, Clinton J. Wang

Current volumetric biomedical foundation models struggle to generalize as public 3D datasets are small and do not cover the broad diversity of medical procedures, conditions, anatomical regions, and imaging protocols. We address this by creating a representation learning method that instead anticipates strong domain shifts at training time itself. We first p

Deepika Gill, Sangeeta Sharma, Sam Shallcross

Pure currents comprise the flow of a two state quantum freedom -- for example the electron spin -- in the absence of charge flow. Radically different from the charge currents that underpin present day electronics, in two dimensional materials possessing additional two state freedoms such as valley index they offer profound possibilities for miniaturization a

Mike Lambeta, Tingfan Wu, Ali Sengul, Victoria Rose Most

Touch is a crucial sensing modality that provides rich information about object properties and interactions with the physical environment. Humans and robots both benefit from using touch to perceive and interact with the surrounding environment (Johansson and Flanagan, 2009; Li et al., 2020; Calandra et al., 2017). However, no existing systems provide rich,

Chaitanya Karamchedu, Matthew Fox, Daniel Gottesman

Assuming the polynomial hierarchy is infinite, we prove a sufficient condition for determining if uniform and polynomial size quantum circuits over a non-universal gate set are not efficiently classically simulable in the weak multiplicative sense. Our criterion exploits the fact that subgroups of $\mathrm{SL}(2;\mathbb{C})$ are essentially either discrete o