December 2024 arXiv papers — page 85

S. I. Ipatov, J. Henrard

The results of the numerical investigations of the evolution of orbits of trans-Neptunian bodies at the 2 : 3 resonance with Neptune are presented. The gravitational influence of the four giant planets was taken into account. For identical initial values of the semimajor axes, eccentricities, and inclinations, but for different initial orbital orientations a

Saheed Popoola, Ashwitha Vollem, Kofi Nti

Computing internships are the most common way for students to gain practical real-world experience. Internships have become a major part of most computing curricula because they help match student abilities or expectations with the demand of the workforce. The internship experience for students vary due to diverse factors such misconceptions about industrial

Florian Kaltenberger, Tommaso Melodia, Irfan Ghauri, Michele Polese

The development of 6G wireless technologies is rapidly advancing, with the 3rd Generation Partnership Project (3GPP) entering the pre-standardization phase and aiming to deliver the first specifications by 2028. This paper explores the OpenAirInterface (OAI) project, an open-source initiative that plays a crucial role in the evolution of 5G and future 6G net

Vasiliki Sideri-Lampretsa, Nil Stolt-Ansó, Huaqi Qiu, Julian McGinnis

Deformable image registration poses a challenging problem where, unlike most deep learning tasks, a complex relationship between multiple coordinate systems has to be considered. Although data-driven methods have shown promising capabilities to model complex non-linear transformations, existing works employ standard deep learning architectures assuming they

S. K. Vankeswaram, V. Kulkarni, S. Deivandren

Spray drop size distribution generated by atomization of fuel influences several facets of a combustion process such as, fuel-air mixing, reaction kinetics and thrust generation. In a typical spray, the drop size distribution evolves spatially, varying significantly between the near and far regions of the spray. Studies so far have focused on either one of t

Soumyasundar Pal, Didier Chételat, Yingxue Zhang, Mark Coates

Large Language Models (LLMs) have exhibited an impressive capability to perform reasoning tasks, especially if they are encouraged to generate a sequence of intermediate steps. Reasoning performance can be improved by suitably combining multiple LLM responses, generated either in parallel in a single query, or via sequential interactions with LLMs throughout

Furkan H. Ilgac, Mounia Bouabdellah, Aydin Sezgin

This paper investigates signle-antenna radar imaging with a reconfigurable intelligent surface (RIS). Configuring phase shifts in a RIS-aided radar system can be thought as synthetic aperture radar (SAR) imaging with a moving virtual source. With this perspective, the problem is modeled in the wavenumber domain and image forming algorithms are formulated for

Kanstantsin Pashkovich, Jacob Skitsko

We study linear contracts for combinatorial problems in multi-agent settings. In this problem, a principal designs a linear contract with several agents, each of whom can decide to take a costly action or not. The principal observes only the outcome of the agents' collective actions, not the actions themselves, and obtains a reward from this outcome. Agents

Hooman Davoudiasl, Hongkai Liu, Roman Marcarelli, Yotam Soreq

A Muon (Synchrotron) Ion Collider (MuSIC) can be the successor to the Electron-Ion Collider at Brookhaven National Laboratory, as well as the ideal demonstrator facility for a future multi-TeV Muon Collider. Besides its rich nuclear physics and Standard Model particle physics programs, in this work we show that the MuSIC with a TeV-scale muon beam offers als

Stephen E. Henrich, Yann Mambrini, Keith A. Olive

We conduct a systematic investigation of freeze-in during reheating while taking care to include both direct and indirect production of dark matter (DM) via gravitational portals and inflaton decay. Direct production of DM can occur via gravitational scattering of the inflaton, while indirect production occurs through scattering in the Standard Model radiati

Olivia M. Bitter, André de Gouvêa, Kevin J. Kelly

We investigate the impact of matter effects on T (time-reversal)-odd observables, making use of the quantum-mechanical formalism of neutrino-flavor evolution. We attempt to be comprehensive and pedagogical. Matter-induced T-invariance violation (TV) is qualitatively different from, and more subtle than, matter-induced CP (charge-parity)-invariance violation.

Sebastian Salazar, Michal Kucer, Yixin Wang, Emily Casleton

This paper introduces posterior mean matching (PMM), a new method for generative modeling that is grounded in Bayesian inference. PMM uses conjugate pairs of distributions to model complex data of various modalities like images and text, offering a flexible alternative to existing methods like diffusion models. PMM models iteratively refine noisy approximati

Michael Bleher

This article provides a review of the gauge-theoretic approach to Khovanov homology, framed in terms of a generalisation of Witten's original proposal. Concretely, the physical arguments underlying Witten's insights suggest that there is a one-parameter family of Haydys-Witten instanton Floer homology groups $HF_{\theta}\bigl(W^4\bigr)$ for four-manifolds. A

Ross McNeill

We study the Born and Inverse Born series for a special case of the second harmonic generation system of PDEs. We give a recursive formula for the forward operators and prove boundedness conditions that guarantee the convergence of the Born and inverse Born series. We also use fixed point theory to give explicit conditions for convergence of the Born series.

Konstantin Zaitsev

In recent years, Large Language Models (LLMs) gain considerable attention for their potential to enhance personalized experiences in virtual assistants and chatbots. A key area of interest is the integration of personas into LLMs to improve dialogue naturalness and user engagement. This study addresses the challenge of persona classification, a crucial compo

Lyle Regenwetter, Cyril Picard, Amin Heyrani Nobari, Akash Srivastava

The field of engineering is shaped by the tools and methods used to solve problems. Optimization is one such class of powerful, robust, and effective engineering tools proven over decades of use. Within just a few years, generative artificial intelligence (GenAI) has risen as another promising tool for general-purpose problem-solving. While optimization shin

Morgan Wack, Kayla Duskin, Damian Hodel

Fact-checking has been promoted as a key method for combating political misinformation. Comparing the spread of election-related misinformation narratives along with their relevant political fact-checks, this study provides the most comprehensive assessment to date of the real-world limitations faced by political fact-checking efforts. To examine barriers to

Mahieyin Rahmun, Rafat Hasan Khan, Tanjim Taharat Aurpa, Sadia Khan

The aim of this project is to implement and design arobust synthetic speech classifier for the IEEE Signal ProcessingCup 2022 challenge. Here, we learn a synthetic speech attributionmodel using the speech generated from various text-to-speech(TTS) algorithms as well as unknown TTS algorithms. Weexperiment with both the classical machine learning methodssuch

Shannon Bernier, Mekhola Sinha, Tyler J. Pearson, Peter V. Sushko

Elucidating the factors limiting quantum coherence in real materials is essential to the development of quantum technologies. Here we report a strategic approach to determine the effect of lattice dynamics on spin coherence lifetimes using oxygen deficient double perovskites as host materials. In addition to obtaining millisecond $T_1$ spin-lattice lifetimes

David Izabel

After recalling the principles that allow space-time to be considered by analogy as an elastic medium, we show how the modified gravity according to the MOND theory concerning the anomaly of the velocities of stars at the periphery of galaxies can be seen as a creep of space acting on the radius of galaxies that gives a creep coefficient of Phi(space) = ((a0

Xiaobing Dai, Zewen Yang

Machine learning is now widely applied across various domains, including industry, engineering, and research. While numerous mature machine learning models have been open-sourced on platforms like GitHub, their deployment often requires writing scripts in specific programming languages, such as Python, C++, or MATLAB. This dependency on particular languages

Chris Hamilton

Fuzzy dark matter (FDM) granulations would drive orbital transport of stars in galactic disks, and in particular would produce roughly equal amounts of radial heating and radial migration. However, observations suggest that heating has been much less efficient than migration in our Galaxy. We argue that this decreases the amount of radial heating, $\mathcal{

Atsushi Yamamura, Surya Ganguli

The deployment of artificial intelligence (AI) in critical decision-making and evaluation processes raises concerns about inherent biases that malicious actors could exploit to distort decision outcomes. We propose a systematic method to reveal such biases in AI evaluation systems and apply it to automated essay grading as an example. Our approach first iden

Martijn Brehm, Jordi Weggemans

For many problems, quantum algorithms promise speedups over their classical counterparts. However, these results predominantly rely on asymptotic worst-case analysis, which overlooks significant overheads due to error correction and the fact that real-world instances often contain exploitable structure. In this work, we employ the hybrid benchmarking method

Andrei Znobishchev, Valerii Filev, Oleg Kudashev, Nikita Orlov

We present CompactFlowNet, the first real-time mobile neural network for optical flow prediction, which involves determining the displacement of each pixel in an initial frame relative to the corresponding pixel in a subsequent frame. Optical flow serves as a fundamental building block for various video-related tasks, such as video restoration, motion estima

Ali R Khojasteh, Lyke K van Dalen, Coen Been, Jerry Westerweel

In an experiment on a turbulent jet, we detect interfacial turbulent layers in a frame that moves, on average, along with the \tnti. This significantly prolongs the observation time of scalar and velocity structures and enables the measurement of two types of Lagrangian coherent structures. One structure, the finite-time Lyapunov field (FTLE), quantifies adv

Luca Arceci, Viacheslav Kuzmin, Rick Van Bijnen

Variational Quantum Algorithms (VQAs) aim at solving classical or quantum optimization problems by optimizing parametrized trial states on a quantum device, based on the outcomes of noisy projective measurements. The associated optimization process benefits from an accurate modeling of the cost function landscape using Gaussian Process Models (GPMs), whose p

Matthew Black, Martin Lang, Alexander Lenz, Zachary Wüthrich

Theory predictions of heavy-hadron lifetime ratios critically depend on precise determinations of the dimension-six spectator effects arising from the double insertion of the weak effective $|\Delta B| = 1$ Hamiltonian. In the presence of beyond-standard-model (BSM) operators, the resulting $\Delta B = 0$ Hamiltonian features additional four-quark operators

Malika Izabachène, Jean-Philippe Bossuat

To derive valuable insights from statistics, machine learning applications frequently analyze substantial amounts of data. In this work, we address the problem of designing efficient secure techniques to probe large datasets which allow a scientist to conduct large-scale medical studies over specific attributes of patients' records, while maintaining the pri

Hossein A. Rahmani, Emine Yilmaz, Nick Craswell, Bhaskar Mitra

The effective training and evaluation of retrieval systems require a substantial amount of relevance judgments, which are traditionally collected from human assessors -- a process that is both costly and time-consuming. Large Language Models (LLMs) have shown promise in generating relevance labels for search tasks, offering a potential alternative to manual

Maria Balanzó-Juandó, Andrea Coladangelo, Remigiusz Augusiak, Antonio Acín

Self-testing refers to the certification of quantum states and measurements based entirely on the correlations exhibited by measurements on separate subsystems. In the bipartite case, self-testing of states has been completely characterized, up to local isometries, as there exist protocols that self-test arbitrary pure states of any local dimension. Much les

Fang Ma, Ju Zhang, Lev Tankelevitch, Payod Panda

Avatars are edging into mainstream videoconferencing, but evaluation of how avatar animation modalities contribute to work meeting outcomes has been limited. We report a within-group videoconferencing experiment in which 68 employees of a global technology company, in 16 groups, used the same stylized avatars in three modalities (static picture, audio-animat

Mengyuan Xiao, Christina C. Williams, Pascal A. Oesch, David Elbaz

We report the discovery of an ultra-massive grand-design red spiral galaxy, named Zh\'ul\'ong (Torch Dragon), at $z_{\rm phot} = 5.2^{+0.3}_{-0.2}$ in the JWST PANORAMIC survey, identified as the most distant bulge+disk galaxy candidate with spiral arms known to date. Zh\'ul\'ong displays an extraordinary combination of properties: 1) a classical bulge cente

Manuel Weber

The Hubbard-Holstein model is one of the central models that describe the competition between electron-electron and electron-phonon interactions. In one dimension and at half-filling, the interplay between an electronic spin-density wave and a phonon-driven charge-density wave is considered to stabilize an intermediate Luther-Emery liquid. Here we show that,

Xu Feng, Guo-yang Li, Yuxuan Jiang, Owen Shortt-Nguyen

Lens tension is essential for accommodative vision but remains difficult to measure with precision. Here, we present an optical coherence elastography (OCE) technique that quantifies both tension and elastic modulus in the lens capsule and underlying tissue. This method derives mechanical parameters from surface wave dispersion across a critical frequency ra

Andrea Ghira, Lorenzo Mai, Simone Marzani

Perturbative calculations for processes involving heavy flavours can be carried out using two approaches: the massive and the massless schemes. These schemes can also be combined to leverage their respective strengths. Additionally, both massive and massless frameworks can be supplemented by soft-gluon resummation. However, matching resummed calculations acr

M. C. Baglio, F. Coti Zelati, A. Di Marco, F. La Monaca

Transitional millisecond pulsars (tMSPs) bridge the evolutionary gap between accreting neutron stars in low-mass X-ray binaries and millisecond radio pulsars. These systems exhibit a unique subluminous X-ray state characterized by the presence of an accretion disk and rapid switches between high and low X-ray emission modes. The high mode features coherent m

Ivan Medina, Oisín Culhane, Felix C. Binder, Gabriel T. Landi

Anomalous thermal relaxation is ubiquitous in nonequilibrium statistical mechanics. An emblematic example of this is the Mpemba effect, where an initially ``hot'' system cools faster than an initially ``cooler'' one. This effect has recently been studied in a variety of different classical and quantum settings. In this Letter, we find a novel signature of th

S. Zarattini, S. Andreon, E. Puddu

The aim of this work is to study the position of gas-rich and gas-poor galaxy clusters within the large-scale structure and, in particular, their distance to filaments. Our sample is built from 29 of the 34 clusters in the X-ray unbiased cluster sample (XUCS), a velocity-dispersion-selected sample for which various properties, including masses, gas fractions

S. Garrappa, E. O. Ofek, S. Ben-Ami, D. Polishook

Transforming the instrumental photometry of ground-based telescopes into a calibrated physical flux in a well-defined passband is a major challenge in astronomy. Along with the intrinsic instrumental difference between telescopes sharing the same filter, the effective transmission is continuously modified by the effects of the variable atmosphere of the Eart

Tanay Kibe, Ayan Mukhopadhyay, Pratik Roy

Utilizing quantum information theory, it has been shown that irreversible entropy production is bounded from both below and above in physical processes. Both these bounds are positive and generalize the Clausius inequality. Such bounds are, however, obtained from distance measures in the space of states, which are hard to define and compute in quantum field

Makoto Sawada, Toshiki Sato, Keiichi Maeda, Koki Itonaga

The origin of the recombining plasma in several Galactic SNRs has been debated. A plausible mechanism would be a rapid cooling in the past, either by adiabatic or conductive process. A recent spectral study of W 49 B reported a possible charge exchange (CX) emission due to collisions between the shock-heated ejecta and external cold clouds, which could be a

Basabendu Barman, Kousik Loho, Óscar Zapata

We explore a purely gravitational origin of observed baryon asymmetry and dark matter (DM) abundance from asymmetric Hawking radiation of light primordial black holes (PBH) in presence of a non-zero chemical potential, originating from the space-time curvature. Considering the PBHs are described by a Reissner-Nordstr\"{o}m metric, and are produced in a radia

Raphaël Bendahan-West, Grant M. Kennedy, David J. A. Brown, Paul A. Strøm

The field of exocomets has been built around the unmatched number of detections made in the circumstellar disc of the archetypal star Beta Pictoris. An exocomet detection in spectroscopy is identified by variable atomic absorption features in a stellar spectrum, associated with transiting gas in and trailing an exocomet coma. This paper presents the largest

Lýdia Štofanová, Aurora Simionescu, Jelle S. Kaastra

In this paper we introduce the clus model, which has been newly implemented in the X-ray spectral fitting software package SPEX. Based on the 3D radial profiles of the gas density, temperature, metal abundance, turbulent, and inflow/outflow velocities, the clus model creates spectra for a chosen projected region on the sky. Additionally, it can also take int

Luca Naterop, Peter Stoffer

We present the first part of a systematic calculation of the two-loop anomalous dimensions in the low-energy effective field theory (LEFT): the effects at dimension five in the power counting. Our calculation is performed in a basis with generic mass matrices, we employ the algebraically consistent 't Hooft-Veltman scheme for $\gamma_5$, and we correct for e

G. Granata, G. B. Caminha, S. Ertl, C. Grillo

We present a spectroscopic analysis of MACS J0138$-$2155, at $z=0.336$, the first galaxy cluster hosting two strongly-lensed supernovae (SNe), Requiem and Encore, providing us with a chance to obtain a reliable $H_0$ measurement from the time delays between the multiple images. We take advantage of new data from the Multi Unit Spectroscopic Explorer (MUSE) o

Oscar Arandes, Emil J. Bergholtz

The remarkable sensitivity of non-Hermitian systems has been extensively studied and stimulated ideas about developing new types of sensors. In this paper, we examine a chain of parametrically driven coupled resonators governed by the squeezed Su-Schrieffer-Heeger model. We emphasize the qualitative difference in sensor performance between configurations dep

Benedikt Placke, Tibor Rakovszky, Nikolas P. Breuckmann, Vedika Khemani

Ordered phases of matter have close connections to computation. Two prominent examples are spin glass order, with wide-ranging applications in machine learning and optimization, and topological order, closely related to quantum error correction. Here, we introduce the concept of topological quantum spin glass (TQSG) order which marries these two notions, exh

Joshua N. Benabou, Claudio Andrea Manzari, Yujin Park, Garima Prabhakar

Heavy axions that couple to both quantum electrodynamics and quantum chromodynamics with masses on the order of MeV - GeV and high-scale decay constants in excess of $\sim$$10^8$ GeV may arise generically in e.g. axiverse constructions. In this work we provide the most sensitive search to-date for the existence of such heavy axions using Fermi-LAT data towar

Raymond T. Co, Taegyu Lee, Sai Chaitanya Tadepalli

We investigate primordial non-Gaussianity (NG) arising from the explicit $U(1)$ symmetry-breaking interactions during inflation involving a nearly massless axial component of a complex scalar field $P$. We analyze the induced NG parameter $f_{\mathrm{NL}}$ under scenarios where the axial field functions as either a curvaton or cold dark matter (CDM). In the

Shreyas Vissapragada, Aida Behmard

The Neptune desert is no longer empty. A handful of close-in planets with masses between those of Neptune and Saturn have now been discovered, and their puzzling properties have inspired a number of interesting theories on the formation and evolution of desert-dwellers. While some studies suggest that Neptune desert planets form and evolve similarly to longe

Mazeyu Ji, Xuanbin Peng, Fangchen Liu, Jialong Li

This paper tackles the challenge of enabling real-world humanoid robots to perform expressive and dynamic whole-body motions while maintaining overall stability and robustness. We propose Advanced Expressive Whole-Body Control (Exbody2), a method for producing whole-body tracking controllers that are trained on both human motion capture and simulated data an

Gaoyang Zhang, Bingtao Fu, Qingnan Fan, Qi Zhang

Text-to-image (T2I) diffusion models excel at generating photorealistic images but often fail to render accurate spatial relationships. We identify two core issues underlying this common failure: 1) the ambiguous nature of data concerning spatial relationships in existing datasets, and 2) the inability of current text encoders to accurately interpret the spa

Yifei Zhou, Qianlan Yang, Kaixiang Lin, Min Bai

The vision of a broadly capable and goal-directed agent, such as an Internet-browsing agent in the digital world and a household humanoid in the physical world, has rapidly advanced, thanks to the generalization capability of foundation models. Such a generalist agent needs to have a large and diverse skill repertoire, such as finding directions between two

Haoyi Jiang, Liu Liu, Tianheng Cheng, Xinjie Wang

3D Semantic Occupancy Prediction is fundamental for spatial understanding, yet existing approaches face challenges in scalability and generalization due to their reliance on extensive labeled data and computationally intensive voxel-wise representations. In this paper, we introduce GaussTR, a novel Gaussian-based Transformer framework that unifies sparse 3D

Matthew Baumgart, Panagiotis Christeas, Jonathan J. Heckman, Rebecca J. Hicks

The string landscape accommodates a broad range of possible effective field theories. This poses a challenge for extracting verifiable predictions as well as falsifiable signatures of string theory. Motivated by these considerations, in this work we observe that all known stringy Standard Models support only low-dimensional representations of the gauge group

Alexander Taveira Blomenhofer, Monique Laurent

We revisit the convergence analysis of two approximation hierarchies for polynomial optimization on the unit sphere. The first one is based on the moment-sos approach and gives semidefinite bounds for which Fang and Fawzi (2021) showed an analysis in $O(1/r^2)$ for the r-th level bound, using the polynomial kernel method. The second hierarchy was recently pr

Maham Tanveer, Yang Zhou, Simon Niklaus, Ali Mahdavi Amiri

By generating plausible and smooth transitions between two image frames, video inbetweening is an essential tool for video editing and long video synthesis. Traditional works lack the capability to generate complex large motions. While recent video generation techniques are powerful in creating high-quality results, they often lack fine control over the deta

Johanna Müller-Horn, Fabian Göttgens, Stefan Dreizler, Sebastian Kamann

Spectroscopic observations of binary stars in globular clusters are essential to shed light on the poorly constrained period, eccentricity, and mass ratio distributions and to develop an understanding of the formation of peculiar stellar objects. 47 Tuc (NGC 104) is one of the most massive Galactic globular clusters, with a large population of blue straggler

Yunzhi Yan, Zhen Xu, Haotong Lin, Haian Jin

This paper aims to tackle the problem of photorealistic view synthesis from vehicle sensor data. Recent advancements in neural scene representation have achieved notable success in rendering high-quality autonomous driving scenes, but the performance significantly degrades as the viewpoint deviates from the training trajectory. To mitigate this problem, we i

Chen Bao, Jiarui Xu, Xiaolong Wang, Abhinav Gupta

How can we predict future interaction trajectories of human hands in a scene given high-level colloquial task specifications in the form of natural language? In this paper, we extend the classic hand trajectory prediction task to two tasks involving explicit or implicit language queries. Our proposed tasks require extensive understanding of human daily activ

Daniel K. Mark, Hong-Ye Hu, Joyce Kwan, Christian Kokail

Understanding the mechanism of high-temperature superconductivity is among the most important problems in physics, for which quantum simulation can provide new insights. However, it remains challenging to characterize superconductivity in existing cold-atom quantum simulation platforms. Here, we introduce a protocol for measuring a broad class of observables

Hsin-Ping Huang, Yang Zhou, Jui-Hsien Wang, Difan Liu

Generating realistic human videos remains a challenging task, with the most effective methods currently relying on a human motion sequence as a control signal. Existing approaches often use existing motion extracted from other videos, which restricts applications to specific motion types and global scene matching. We propose Move-in-2D, a novel approach to g

Chenglin Li, Guangchun Ruan, Hua Geng

Safe reinforcement learning (RL) is a popular and versatile paradigm to learn reward-maximizing policies with safety guarantees. Previous works tend to express the safety constraints in an expectation form due to the ease of implementation, but this turns out to be ineffective in maintaining safety constraints with high probability. To this end, we move to t

Guoxing Sun, Rishabh Dabral, Heming Zhu, Pascal Fua

Real-time free-view human rendering from sparse-view RGB inputs is a challenging task due to the sensor scarcity and the tight time budget. To ensure efficiency, recent methods leverage 2D CNNs operating in texture space to learn rendering primitives. However, they either jointly learn geometry and appearance, or completely ignore sparse image information fo

Justo Antonio Gonzalez Villalba, Klaus Dolag, Veronica Biffi

Our aim is to understand how the interplay between AGN feedback and merge processes can effectively turn cool-core galaxy clusters into hot-core clusters in the modern universe. Additionally, we also aim to clarify which parameters of the AGN feedback model used in simulations can cause an excess of feedback at the scale of galaxy groups while not efficientl

Keith Burghardt, Johannes H. Uhl, Kristina Lerman, Stefan Leyk

Despite the rapid growth of cities in the past century, our quantitative, in-depth understanding of how cities grow remains limited due to a consistent lack of historical data. Thus, the scaling laws between a city's features and its population as they evolve over time, known as temporal city scaling, is under-explored, especially for time periods spanning m

Mark Endo, Xiaohan Wang, Serena Yeung-Levy

Recent works on accelerating Vision-Language Models achieve strong performance across a variety of vision-language tasks despite highly compressing visual information. In this work, we examine the popular acceleration approach of early pruning of visual tokens inside the language model. Surprisingly, we find that while strong performance is maintained across

Youssef A. Ait Alama, Sampada Sakpal, Ke Wang, Razvan Bunescu

Hardware failures are a growing challenge for machine learning accelerators, many of which are based on systolic arrays. When a permanent hardware failure occurs in a systolic array, existing solutions include localizing and isolating the faulty processing element (PE), using a redundant PE for re-execution, or in some extreme cases decommissioning the entir

Sheng Yin, Xianghe Pang, Yuanzhuo Ding, Menglan Chen

With the integration of large language models (LLMs), embodied agents have strong capabilities to understand and plan complicated natural language instructions. However, a foreseeable issue is that those embodied agents can also flawlessly execute some hazardous tasks, potentially causing damages in the real world. Existing benchmarks predominantly overlook

Yuri N. Lima, Andre V. Giannini, Victor P. B. Goncalves

It is known that the proton is overpopulated by gluons and is characterized as a highly dense medium at high collision energies. From this, the formation of a new state of matter called Color Glass Condensate (CGC) is expected, and an open question is whether the nonlinear effects predicted by this state are identifiable at the LHC. The multiplicity of parti

Andrea Dunn Beltran, Daniel Rho, Marc Niethammer, Roni Sengupta

Simultaneous Localization and Mapping (SLAM) systems typically assume static, distant illumination; however, many real-world scenarios, such as endoscopy, subterranean robotics, and search & rescue in collapsed environments, require agents to operate with a co-located light and camera in the absence of external lighting. In such cases, dynamic near-field lig

Miriam Wanner, Benjamin Van Durme, Mark Dredze

The decompose-then-verify strategy for verification of Large Language Model (LLM) generations decomposes claims that are then independently verified. Decontextualization augments text (claims) to ensure it can be verified outside of the original context, enabling reliable verification. While decomposition and decontextualization have been explored independen

Jui-Che Chiang, Hou-Ning Hu, Bo-Syuan Hou, Chia-Yu Tseng

Although facial landmark detection (FLD) has gained significant progress, existing FLD methods still suffer from performance drops on partially non-visible faces, such as faces with occlusions or under extreme lighting conditions or poses. To address this issue, we introduce ORFormer, a novel transformer-based method that can detect non-visible regions and r

Siqi Li, Xiaoxue Chen, Haoyu Cheng, Guyue Zhou

Detecting the openable parts of articulated objects is crucial for downstream applications in intelligent robotics, such as pulling a drawer. This task poses a multitasking challenge due to the necessity of understanding object categories and motion. Most existing methods are either category-specific or trained on specific datasets, lacking generalization to

Victor Olkhov

This paper derives the expressions of correlations between prices of two assets, returns of two assets, and price-return correlations of two assets that depend on statistical moments and correlations of the current values, past values, and volumes of their market trades. The usual frequency-based expressions of correlations of time series of prices and retur

Maximilian Weiherer, Antonia von Riedheim, Vanessa Brébant, Bernhard Egger

We present the first deep implicit 3D shape model of the female breast, building upon and improving the recently proposed Regensburg Breast Shape Model (RBSM). Compared to its PCA-based predecessor, our model employs implicit neural representations; hence, it can be trained on raw 3D breast scans and eliminates the need for computationally demanding non-rigi

Jeffrey Cheng, Benjamin Van Durme

Chain-of-thought (CoT) decoding enables language models to improve reasoning performance at the cost of high generation latency in decoding. Recent proposals have explored variants of contemplation tokens, a term we introduce that refers to special tokens used during inference to allow for extra computation. Prior work has considered fixed-length sequences d

Yifei Duan, Liu Li, Zirui Zhai, Jinxia Yao

We applied few-shot in-context learning on the OPT-1.3B model for the natural language inference task and employed knowledge distillation to internalize the context information, reducing model parameter from 1.3B to 125M and achieving a size reduction from 2.5GB to 0.25GB. Compared to using in-context learning alone on similarly sized models, this context di

Hamid Shabani, Avik De, Tee-How Loo

One resolution of the ancient cosmic singularity, i.e., the Big Bang Singularity (BBS), is to assume an inflationary stage preceded by a long enough static state in which the universe and its physical properties would oscillate around certain equilibrium points. The early period is referred to as the Einstein Static (ES) Universe phase, which characterizes a

Hongrui Jin

This article methodologically reflects on how social media scholars can effectively engage with speech-based data in their analyses. While contemporary media studies have embraced textual, visual, and relational data, the aural dimension remained comparatively under-explored. Building on the notion of secondary orality and rejection towards purely visual cul

Bolei Ma, Berk Yoztyurk, Anna-Carolina Haensch, Xinpeng Wang

In recent research, large language models (LLMs) have been increasingly used to investigate public opinions. This study investigates the algorithmic fidelity of LLMs, i.e., the ability to replicate the socio-cultural context and nuanced opinions of human participants. Using open-ended survey data from the German Longitudinal Election Studies (GLES), we promp

Xingjian Wang, Li Chai

In-the-wild dynamic facial expression recognition (DFER) encounters a significant challenge in recognizing emotion-related expressions, which are often temporally and spatially diluted by emotion-irrelevant expressions and global context. Most prior DFER methods directly utilize coupled spatiotemporal representations that may incorporate weakly relevant feat

Gautam Venugopalan, Clarke A. Hardy, Kenneth Kohn, Yuqi Zhu

The search for new gravity-like interactions at the sub-millimeter scale is a compelling area of research, with important implications for the understanding of classical gravity and its connections with quantum physics. We report improved constraints on Yukawa-type interactions in the $10\,\mathrm{\mu m}$ regime using optically levitated dielectric microsphe

Olaf Post, Sebastian Zimmer

The aim of this article is to define and compare several distances (or metrics) between operators acting on different (separable) Hilbert spaces. We consider here three main cases of how to measure the distance between two bounded operators: first by taking the distance between their unitary orbits, second by isometric embeddings (this generalises a concept

Lukas Brenner, Libor Caha, Xavier Coiteux-Roy, Robert Koenig

A common starting point of traditional quantum algorithm design is the notion of a universal quantum computer with a scalable number of qubits. This convenient abstraction mirrors classical computations manipulating finite sets of symbols, and allows for a device-independent development of algorithmic primitives. Here we advocate an alternative approach cent

Parker Addison, Minh-Tuan H. Nguyen, Tomislav Medan, Jinali Shah

Organizations seeking to utilize Large Language Models (LLMs) for knowledge querying and analysis often encounter challenges in maintaining an LLM fine-tuned on targeted, up-to-date information that keeps answers relevant and grounded. Retrieval Augmented Generation (RAG) has quickly become a feasible solution for organizations looking to overcome the challe

Sebastian von Hausegger, Charles Dalang

The dipole anisotropy induced by our peculiar motion in the sky distribution of cosmologically distant sources is an important consistency test of the standard FLRW cosmology. In this work, we formalize how to compute the kinematic matter dipole in redshift bins. Apart from the usual terms arising from angular aberration and flux boosting, there is a contrib

Mohammad Nazmush Shamael, Sabila Nawshin, Swakkhar Shatabda, Salekul Islam

This work presents the BanglishRev Dataset, the largest e-commerce product review dataset to date for reviews written in Bengali, English, a mixture of both and Banglish, Bengali words written with English alphabets. The dataset comprises of 1.74 million written reviews from 3.2 million ratings information collected from a total of 128k products being sold i

Chukwudubem Umeano, Stefano Scali, Oleksandr Kyriienko

We propose a quantum algorithm for calculating the structural properties of complex networks and graphs. The corresponding protocol -- deteQt -- is designed to perform large-scale community and botnet detection, where a specific subgraph of a larger graph is identified based on its properties. We construct a workflow relying on ground state preparation of th

Jiale Liu, Yifan Zeng, Malte Højmark-Bertelsen, Marie Normann Gadeberg

Traditional enterprises face significant challenges in processing business documents, where tasks like extracting transport references from invoices remain largely manual despite their crucial role in logistics operations. While Large Language Models offer potential automation, their direct application to specialized business domains often yields unsatisfact

Junyu Cao

In many data-driven decision-making problems, performance guarantees often depend heavily on the correctness of model assumptions, which may frequently fail in practice. We address this issue in the context of a feature-based newsvendor problem, where demand is influenced by observed features such as demographics and seasonality. To mitigate the impact of mo

Ilya Rozenfeld

As the use of complex machine learning models continues to grow, so does the need for reliable explainability methods. One of the most popular methods for model explainability is based on Shapley values. There are two most commonly used approaches to calculating Shapley values which produce different results when features are correlated, conditional and marg

Juan Del Aguila Ferrandis, João Moura, Sethu Vijayakumar

Manipulation without grasping, known as non-prehensile manipulation, is essential for dexterous robots in contact-rich environments, but presents many challenges relating with underactuation, hybrid-dynamics, and frictional uncertainty. Additionally, object occlusions in a scenario of contact uncertainty and where the motion of the object evolves independent

Yimu Pan, Sitao Zhang, Alison D. Gernand, Jeffery A. Goldstein

Robustness and generalizability in medical image segmentation are often hindered by scarcity and limited diversity of training data, which stands in contrast to the variability encountered during inference. While conventional strategies -- such as domain-specific augmentation, specialized architectures, and tailored training procedures -- can alleviate these

Lu Liu, Huiyu Duan, Qiang Hu, Liu Yang

Artificial intelligence generative models exhibit remarkable capabilities in content creation, particularly in face image generation, customization, and restoration. However, current AI-generated faces (AIGFs) often fall short of human preferences due to unique distortions, unrealistic details, and unexpected identity shifts, underscoring the need for a comp

E. Hadad, T. Mazeh, S. Faigler, A. G. A. Brown

The Gaia DR3 catalogue includes line-broadening measurements (vbroad) for 3524677 stars. We concentrate here on the low-mass main-sequence sub-sample of the catalogue, with BP-RP in the range 1-1.6, which includes 81371 sources. The colour-magnitude diagram of the sample displays two distinct strips, the brighter of which is probably mostly composed of unres

Anisa Khatun

We present recent results from the ALICE Collaboration on the study of coherent and incoherent J/$\psi$ photoproduction in ultra-peripheral Pb-Pb collisions, including results from exclusive and dissociate J/$\psi$ mesons in ultra-peripheral p-Pb interactions. These measurements provide unique insights into the initial state of protons and ions, with great s

Paolo Gabriel, Peter Rehani, Tyler Troy, Tiffany Wyatt

This study introduces an AI-driven platform for continuous and passive patient monitoring in hospital settings, developed by LookDeep Health. Leveraging advanced computer vision, the platform provides real-time insights into patient behavior and interactions through video analysis, securely storing inference results in the cloud for retrospective evaluation.