November 2024 arXiv papers — page 130

Sicelukwanda Zwane, Daniel Cheney, Curtis C. Johnson, Yicheng Luo

Soft robots offer more flexibility, compliance, and adaptability than traditional rigid robots. They are also typically lighter and cheaper to manufacture. However, their use in real-world applications is limited due to modeling challenges and difficulties in integrating effective proprioceptive sensors. Large-scale soft robots ($\approx$ two meters in lengt

Stephen H. Yuwono, Run R. Li, Tianyuan Zhang, Xiaosong Li

We present an implementation of relativistic ionization-potential (IP) equation-of-motion coupled-cluster (EOMCC) with up to 3-hole--2-particle (3h2p) excitations that makes use of the molecular mean-field exact two-component (mmfX2C) framework and the full Dirac--Coulomb--Breit Hamiltonian. The closed-shell nature of the reference state in an X2C-IP-EOMCC c

Neeratyoy Mallik, Maciej Janowski, Johannes Hog, Herilalaina Rakotoarison

Scaling model sizes to scale performance has worked remarkably well for the current large language models paradigm. The research and empirical findings of various scaling studies led to novel scaling results and laws that guides subsequent research. High training costs for contemporary scales of data and models result in a lack of thorough understanding of h

Enrique Muñoz, Juan Pablo Esparza, José Braun, Rodrigo Soto-Garrido

Starting from a microscopic model for the particle-particle interactions in a Weyl semimetal, we analyzed the possibility for conventional as well as monopole Cooper pairing between quasiparticle excitations at the same (intra-nodal) or opposite (inter-nodal) Weyl nodes. We derived a coupled system of self-consistent BCS-like equations, where the angular dep

Radovan Dermisek, Keith Hermanek

We derive a complete set of Feynman rules in the general two-Higgs doublet model effective field theory where the effects of additional new physics are parametrized by operators up to mass dimension-six. We calculate the physical Higgs spectrum, contributions to the couplings and masses of electroweak gauge bosons and fermions, and all contact interactions a

Bardiya Akhbari, Manish Gawali, Nicholas A. Dronen

Set theory is foundational to mathematics and, when sets are finite, to reasoning about the world. An intelligent system should perform set operations consistently, regardless of superficial variations in the operands. Initially designed for semantically-oriented NLP tasks, large language models (LLMs) are now being evaluated on algorithmic tasks. Because se

Konstantinos Kontras, Thomas Strypsteen, Christos Chatzichristos, Paul Pu Liang

Multimodal learning holds promise for richer information extraction by capturing dependencies across data sources. Yet, current training methods often underperform due to modality competition, a phenomenon where modalities contend for training resources leaving some underoptimized. This raises a pivotal question: how can we address training imbalances, ensur

Naotaka Kubo, Tomoki Nosaka, Yi Pang

In this paper we study the partition function of the mass deformed ABJM theory on a squashed three sphere. In particular, we focus on the case with the Chern-Simons levels being $\pm 1$ and apply a duality between this theory and the $\mathcal{N}=4$ $\mathrm{U}\left(N\right)$ super Yang-Mills theory with an adjoint hypermultiplet and a fundamental hypermulti

Zlatinka I. Dimitrova

We apply the Simple Equations Method (SEsM) for obtaining exact solutions of nonlinear differential equations. We discuss several examples with goal to illustrate the results from the use of derivatives of composite functions in the algorithm of SEsM. The discussed examples contain derivatives of functions which are composite functions of solutions of two si

B. N. J. Persson, R. Xu, N. Miyashita

We study the wear rate (mass loss per unit sliding distance) of a tire tread rubber compound sliding on concrete paver surfaces under dry and wet conditions, at different nominal contact pressures of $\sigma_0 = 0.12$, $0.29$, and $0.43 \ {\rm MPa}$, and sliding velocities ranging from $v= 1 \ {\rm \mu m/s}$ to $1 \ {\rm cm/s}$. We find that the wear rate is

Giulio Crisanti, Burkhard Eden, Maximilian Gottwald, Pierpaolo Mastrolia

Higher-point functions in N = 4 super Yang-Mills theory can be constructed using integrability by triangulating the surfaces on which Feynman graphs would be drawn. It remains hard to analytically compute the necessary re-gluing of the tiles by virtual particles. We propose a new approach to study a series of residues encountered in the two-particle gluing o

Mykola I. Sloika, Yuri Gaididei, Volodymyr P. Kravchuk, Oleksandr V. Pylypovskyi

By tailoring geometrical properties of magnetic nanocap structures, new possibilities appear to control its magnetic properties, such as the dynamics of magnetic vortices. Here, we develop an approach to describe dynamics of magnetic vortices on a spherical cap. Analytic results for the gyrofrequency of the vortex state are in a good agreement with micromagn

Benjamin Briggs, Srikanth B. Iyengar, Greg Stevenson

We develop a correspondence between presentations of compactly generated triangulated categories as localizations of derived categories of ring spectra and proxy-small objects, and explore some consequences. In addition, we give a characterization of proxy-smallness in terms of coproduct preservation of the associated corepresentable functor `up to base chan

Fernando de Melo, Gabriel Dias Carvalho, Pedro S. Correia, Paola Concha Obando

Quantum mechanics started out as a theory to describe the smallest scales of energy in Nature. After a hundred years of development it is now routinely employed to describe, among others, quantum computers with thousands of qubits. This tremendous progress turns the debate of foundational questions into a technological imperative. In what follows we introduc

Yinshuang Xu, Dian Chen, Katherine Liu, Sergey Zakharov

Incorporating inductive bias by embedding geometric entities (such as rays) as input has proven successful in multi-view learning. However, the methods adopting this technique typically lack equivariance, which is crucial for effective 3D learning. Equivariance serves as a valuable inductive prior, aiding in the generation of robust multi-view features for 3

Nicholas Bauman, Libor Veis, Karol Kowalski, Jiri Brabec

The Density Matrix Renormalization Group (DMRG) method has become a prominent tool for simulating strongly correlated electronic systems characterized by dominant static correlation effects. However, capturing the full scope of electronic interactions, especially for complex chemical processes, requires an accurate treatment of static and dynamic correlation

A. Montes-Rodríguez, J. A. Virtanen

We give very simple proofs of the classical results of Magnus and Hill on the spectral properties of the Hilbert matrix $$ H = \left ( {1 \over i+j+ 1 } \right )_{i,j\geq 0} $$ which defines a bounded linear operator on the sequence space $\ell^2$. In particular, we use the Mehler-Fock transform to find the spectrum and the latent eigenfunctions of the Hilbe

Sven Banisch, Joris Wessels

Understanding how individuals' beliefs and attitudes evolve within a population is crucial for explaining social phenomena such as polarization and consensus formation. We explore a persuasive arguments model incorporating confirmation bias, where individuals preferentially accept information aligning with their existing beliefs. By employing a mean-field ap

Arianna Bunnell, Dustin Valdez, Fredrik Strand, Yannik Glaser

Background. Breast cancer screening programs using mammography have led to significant mortality reduction in high-income countries. However, many low- and middle-income countries lack resources for mammographic screening. Handheld breast ultrasound (BUS) is a low-cost alternative but requires substantial training. Artificial intelligence (AI) enabled BUS ma

Emanuele De Rubeis, Giovanni Lacopo, Claudio Gheller, Luca Tornatore

This paper presents an implementation of radio astronomy imaging algorithms on modern High Performance Computing (HPC) infrastructures, exploiting distributed memory parallelism and acceleration throughout multiple GPUs. Our code, called RICK (Radio Imaging Code Kernels), is capable of performing the major steps of the w-stacking algorithm presented in Offri

Kirti Bhagat, Kinshuk Vasisht, Danish Pruthi

While a large body of work inspects language models for biases concerning gender, race, occupation and religion, biases of geographical nature are relatively less explored. Some recent studies benchmark the degree to which large language models encode geospatial knowledge. However, the impact of the encoded geographical knowledge (or lack thereof) on real-wo

Lukáš Frk, Pavel Baláž, Elguja Archemashvili, Martin Žonda

We apply various unsupervised machine learning methods for phase classification to investigate the finite-temperature phase diagram of the spinless Falicov-Kimball model in two dimensions. Using only particle occupation snapshots from Monte Carlo simulations as input, each technique, including a straightforward classification based on principal component ana

J. T. Quartuccio, P. H. R. S. Moraes, G. N. Zeminiani, M. M. Lapola

We construct equilibrium configurations for neutron stars using a specific $f(R,T)$ functional form, recently derived through gaussian process applied to measurements of the Hubble parameter. By construction, this functional form serves as an alternative explanation for cosmic acceleration, circumventing the cosmological constant problem. Here, we aim to exa

Remington G. Gerras, Thomas Alford, Michael J. Randall, Joseph Seibert

The Simons Observatory is a ground-based telescope array located at an elevation of 5200 meters, in the Atacama Desert in Chile, designed to measure the temperature and polarization of the cosmic microwave background. It comprises four telescopes: three 0.42-meter small aperture telescopes (SATs), focused on searching for primordial gravitational waves, and

Trisha Das, Zifeng Wang, Afrah Shafquat, Mandis Beigi

Each year, hundreds of clinical trials are conducted to evaluate new medical interventions, but sharing patient records from these trials with other institutions can be challenging due to privacy concerns and federal regulations. To help mitigate privacy concerns, researchers have proposed methods for generating synthetic patient data. However, existing appr

Dipam Das, Debasish Bhattacharjee

In this study, an odour-mediated system is developed and studied. In an odor-mediated systems, the sense of smell or odour of species plays a critical role in the interactions between predators and prey. It is widely recognised in scientific literature that these systems are very common and essential across natural ecosystems. These systems are crucial for v

Ethan Weinberger, Ryan Conrad, Tal Ashuach

Genetic screens mediated via CRISPR-Cas9 combined with high-content readouts have emerged as powerful tools for biological discovery. However, computational analyses of these screens come with additional challenges beyond those found with standard scRNA-seq analyses. For example, perturbation-induced variations of interest may be subtle and masked by other d

Hernan L. Calvo, Luis E. F. Foa Torres, Matias Berdakin

While intense laser irradiation and moir\'e engineering have independently proven powerful for tuning material properties on demand in condensed matter physics, their combination remains unexplored. Here we exploit tilted laser illumination to create spatially modulated light-matter interactions, leading to two striking phenomena in graphene. First, using tw

Amith Khandakar, David G. Michelson, Mansura Naznine, Abdus Salam

Severe collisions can result from aggressive driving and poor road conditions, emphasizing the need for effective monitoring to ensure safety. Smartphones, with their array of built-in sensors, offer a practical and affordable solution for road-sensing. However, the lack of reliable, standardized datasets has hindered progress in assessing road conditions an

Jericho Cain, Hayden Beadles, Karthik Venkatesan

Okta logs are used today to detect cybersecurity events using various rule-based models with restricted look back periods. These functions have limitations, such as a limited retrospective analysis, a predefined rule set, and susceptibility to generating false positives. To address this, we adopt unsupervised techniques, specifically employing autoencoders.

Roman Schnabel, Nergis Mavalvala, David E. McClelland, Ping Koy Lam

Einstein's General Theory of Relativity predicts that accelerating mass distributions produce gravitational radiation, analogous to electromagnetic radiation from accelerating charges. These gravitational waves have not been directly detected to date, but are expected to open a new window to the Universe in the near future. Suitable telescopes are kilometre-

Lilian L. Lee, Natascha M. Förster Schreiber, Sedona H. Price, Daizhong Liu

Spatially-resolved emission line kinematics are invaluable to investigating fundamental galaxy properties and have become increasingly accessible for galaxies at $z\gtrsim0.5$ through sensitive near-infrared imaging spectroscopy and millimeter interferometry. Kinematic modeling is at the core of the analysis and interpretation of such data sets, which at hig

Haoyu Yang, Haoxing Ren

Inverse Lithography Technology (ILT) has emerged as a promising solution for photo mask design and optimization. Relying on multi-beam mask writers, ILT enables the creation of free-form curvilinear mask shapes that enhance printed wafer image quality and process window. However, a major challenge in implementing curvilinear ILT for large-scale production is

Innes Bigaran, Joshua Isaacson, Taegyun Kim, Karla Tame-Narvaez

We explore the phenomenological impact of interference in tree-level contributions to three-body final states in $2\to 3$ scattering processes. This work introduces a novel search strategy leveraging asymmetries to enable sensitivity to CP-violating effects in less well-explored regions of phase space. Analytically, we demonstrate the effectiveness of this o

Denielle Ricciardi, D. Tom Seidl, Brian Lester, Amanda Jones

Accurate material characterization and model calibration are essential for computationally-supported engineering decisions. Current characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data is collected for a specific model of interest, (3) use deterministi

Jun Wang, Zhi Qiao, Wenlong Zhang, Suyi Li

Over the past decades, we have witnessed a rapid emergence of soft and reconfigurable robots thanks to their capability to interact safely with humans and adapt to complex environments. However, their softness makes accurate control very challenging. High-fidelity sensing is critical in improving control performance, especially posture and contact estimation

Tanzim Mahfuz, Swarup Bhunia, Prabuddha Chakraborty

Design and manufacturing of integrated circuits predominantly use a globally distributed semiconductor supply chain involving diverse entities. The modern semiconductor supply chain has been designed to boost production efficiency, but is filled with major security concerns such as malicious modifications (hardware Trojans), reverse engineering (RE), and clo

Elsa Perez-Martin, Tristan Beranger, Laurent Bonnet, Frederic Teppe

Electrodynamic interactions between biomolecules are of potential biological interest for signaling warranting investigation of their activation through various mechanisms in living systems. Here, using as model system a light harvesting protein within the phycobilisome antenna system of red algae, we proved that not only light exposure but also thermal ener

Hannah McAleese, Anuj Agrawal, Vivek Vasan, Conall J. Campbell

Distributing entanglement over long distances remains a challenge due to its fragility when exposed to environmental effects. In this work, we compare various entanglement distribution protocols in a realistic noisy fiber network. We focus specifically on two schemes that only require the sending of a non-entangled carrier photon to remote nodes of the netwo

William Roster, Mara Salvato, Sven Krippendorf, Aman Saxena

Computing photo-z for AGN is challenging, primarily due to the interplay of relative emissions associated with the SMBH and its host galaxy. SED fitting methods, effective in pencil-beam surveys, face limitations in all-sky surveys with fewer bands available, lacking the ability to capture the AGN contribution to the SED accurately. This limitation affects t

Samsuzzaman Afroz, Suvodip Mukherjee

Gravitational Wave (GW) sources offer a valuable window to the physical processes that govern the formation of binary compact objects (BCOs). However, deciphering such information from GW data is substantially challenging due to the difficulty in mapping from the space of observation to the space of numerous theoretical models. We introduce the concept of BC

Arjun B. Savel, Megan Bedell, Eliza M. -R. Kempton, Peter Smith

Ground-based high-resolution cross-correlation spectroscopy (HRCCS; R >~ 15,000) is a powerful complement to space-based studies of exoplanet atmospheres. By resolving individual spectral lines, HRCCS can precisely measure chemical abundance ratios, directly constrain atmospheric dynamics, and robustly probe multidimensional physics. But the subtleties of HR

J. M. Diederik Kruijssen, Renata Valieva, Kenneth Peluso, Nicholas Emmons

In decentralized systems, it is often necessary to select an 'active' subset of participants from the total participant pool, with the goal of satisfying computational limitations or optimizing resource efficiency. This selection can sometimes be made at random, mirroring the sortition practice invented in classical antiquity aimed at achieving a high degree

Daniele Sorini, John A. Peacock, Lucas Lombriser

We present an extended analytic model for cosmic star formation, with the aim of investigating the impact of cosmological parameters on the star formation history within the $\Lambda$CDM paradigm. Constructing an ensemble of flat $\Lambda$CDM models where the cosmological constant varies between $\Lambda = 0$ and $10^5$ times the observed value, $\Lambda_{\r

Tejas Satish Gotavade

This research introduces an innovative artificial intelligence-driven educational concept designed to optimize self-directed learning through personalized course delivery and automated teaching assistance. The system leverages fine-tuned AI models to create an adaptive learning environment that encompasses customized roadmaps, automated presentation generati

Arun Debray, Matthew Yu

We look at a new string$^h$ tangential structure first introduced by Devalapurkar and relate it to the $W_7=0$ condition of Diaconescu-Moore-Witten for type IIA string theory and M-theory. We show that a string$^h$ structure on the target space automatically satisfies the $W_7=0$ condition and we also explain when the $W_7=0$ condition gives rise to a string

Cheryne Jonay, Cathy Li, Tianci Zhou

The out-of-time ordered correlator (OTOC) has become a popular probe for quantum information spreading and thermalization. In systems with local interactions, the OTOC defines a characteristic butterfly lightcone that separates a regime not yet disturbed by chaos from one where time-evolved operators and the OTOC approach their equilibrium value. This relaxa

Parvinder Solanki, Fabrizio Minganti

While a generic open quantum system decays to its steady state, continuous time crystals (CTCs) develop spontaneous oscillation and never converge to a stationary state. Just as crystals develop correlations in space, CTCs do so in time. Here, we introduce a Continuous Quasi Time Crystals (CQTC). Despite being characterized by the presence of non-decaying os

Stefano Antonini, Vijay Balasubramanian, Ning Bao, ChunJun Cao

We establish an equivalence between non-isometry of quantum codes and state-dependence of operator reconstruction, and discuss implications of this equivalence for holographic duality. Specifically, we define quantitative measures of non-isometry and state-dependence and describe bounds relating these quantities. In the context of holography we show that, as

Gary T. Horowitz, Jorge E. Santos

We show that the general charged, rotating black hole in five-dimensional Einstein-Maxwell theory has a singular extremal limit. Only the known analytic solutions with exactly zero charge or zero angular momenta have smooth extremal horizons. We also consider general black holes in five-dimensional Einstein-Maxwell-Chern-Simons theory, and show that they als

Brian Batell, Amit Bhoonah, Wenjie Huang

Heavy right-handed neutrinos are highly motivated due to their connection with the origin of neutrino masses via the seesaw mechanism. If the right-handed neutrino Majorana mass is at or below the weak scale, direct experimental discovery of these states is possible in laboratory experiments. However, there is no a priori basis to expect right-handed neutrin

Dario Antolini, Nick Early

Recent developments in particle physics have revealed deep connections between scattering amplitudes and tropical geometry. From the heart of this relationship emerged the chirotropical Grassmannian $\text{Trop}^\chi \text{G}(k,n)$ and the chirotropical Dressian $\text{Dr}^\chi(k,n)$, polyhedral fans built from uniform realizable chirotopes that encode the c

Hanbit Oh, Hui Yang, Ya-Hui Zhang

Superconductivity is usually assumed to arise from attractive interaction. In this work we show that strong pairing is possible soley from kinetic energy even without a net attraction. We demonstrate a high-temperature kinetic superconductor in a simple lattice model with nearest-neighbor hopping ($t$) projected onto a constrained Hilbert space, analogous to

Michael J. Nicandro Rosenthal, Amy J. Barger, Lennox L. Cowie, Logan H. Jones

We present the results of a Keck and NOEMA spectroscopic survey of 507 galaxies, where we confirm the presence of two massive overdensities at $z = 3.090 - 3.110$ and $z = 3.133 - 3.155$ in the neighborhood of the GOODS-N, each with over a dozen spectroscopically confirmed members. We find that both of these have galaxy overdensities of NIR-detected galaxies

Hannah E. Ambrose, A. P. Whitworth

We explore the relative percentages of binary systems and higher-order multiples that are formed by pure stellar dynamics, within a small subcluster of $N$ stars. The subcluster is intended to represent the fragmentation products of a single isolated core, after most of the residual gas of the natal core has dispersed. Initially the stars have random positio

A. Bolamperti, C. Grillo, G. B. Caminha, G. Granata

Systems where multiple sources at different redshifts are strongly lensed by the same deflector allow one to directly investigate the evolution of the angular diameter distances with redshift, and thus to learn about the geometry of the Universe. We present measurements of the values of the total matter density, $\Omega_m$, and of the dark energy equation of

Shadi Ali Ahmad, Marc S. Klinger

Carrying the insights of conditional probability to the quantum realm is notoriously difficult due to the non-commutative nature of quantum observables. Nevertheless, conditional expectations on von Neumann algebras have played a significant role in the development of quantum information theory, and especially the study of quantum error correction. In quantu

Kevin Zhou, Tomas Brauner

Electric fields are commonly visualized with field line diagrams, which only unambiguously specify the field's direction. We consider two simple questions. First, can one deduce if an electric field is conservative, as required e.g. in electrostatics, from its field lines alone? Second, are there conservative electric fields with straight field lines, beside

Benjamin A. Hyatt, Daniel Lecoanet, Evan H. Anders, Keaton J. Burns

The susceptibility of timestepping algorithms to numerical instabilities is an important consideration when simulating partial differential equations (PDEs). Here we identify and analyze a pernicious numerical instability arising in pseudospectral simulations of nonlinear wave propagation resulting in finite-time blow-up. The blow-up time scale is independen

Boris Post, Ioannis Tsiares

We use the Virasoro TQFT to derive an integral identity that we view as a non-rational generalization of the Verlinde formula for the Virasoro algebra with central charge $c\geq 25$. The identity expresses the Virasoro fusion kernel as an integral over a ratio of modular S-kernels on the (punctured) torus. In particular, it shows that the one-point S-kernel

Víctor Jaramillo, Darío Núñez, Milton Ruiz, Miguel Zilhão

Gauged boson stars are exotic compact objects that can potentially mimic black holes or magnetized neutron stars in both their gravitational and electromagnetic signatures, offering a compelling new description or even an alternative explanation for various multimessenger phenomena. As a crucial step toward establishing boson stars as viable multimessenger s

José María Arroyo-Polonio, Giuseppina Battaglia, Guillaume F. Thomas, Raffaele Pascale

Aims. Recently, both the presence of multiple stellar chemo-kinematic components and rotation in the Sculptor dwarf spheroidal galaxy have been put into question. Therefore, we re-examine the chemo-kinematic properties of this galaxy making use of the best spectroscopic data-set available containing both line-of-sight velocities and metallicities of individu

Harley Katz, Martin P. Rey, Corentin Cadiou, Taysun Kimm

We introduce MEGATRON, a new galaxy formation model for cosmological radiation hydrodynamics simulations of high-redshift galaxies. The model accounts for the non-equilibrium chemistry and heating/cooling processes of $\geq 80$ atoms, ions, and molecules, coupled to on-the-fly radiation transfer. We apply the model in a cosmological setting to the formation

F. Hammer, Y. J. Jiao, G. A. Mamon, Y. B. Yang

Galactic halos are known to grow hierarchically, inside out. This implies a correlation between the infall lookback time of satellites and their binding energy. Cosmological simulations predict a linear relation between the infall lookback time and the logarithm of the binding energy, with a small scatter. Gaia measurements of the bulk proper motions of glob

Ji-Jia Tang, Christian Wolf, John Tonry

The stochastic photometric variability of quasars is known to follow a random-walk phenomenology on emission timescales of months to years. Some high-cadence restframe optical monitoring in the past has hinted at a suppression of variability amplitudes on shorter timescales of a few days or weeks, opening the question of what drives the suppression and how i

Gabriel Sasseville, Julie Hlavacek-Larrondo, Samantha C. Berek, Gwendolyn M. Eadie

The strongest and most universal scaling relation between a supermassive black hole and its host galaxy is known as the $M_\bullet-\sigma$ relation, where $M_\bullet$ is the mass of the central black hole and $\sigma$ is the stellar velocity dispersion of the host galaxy. This relation has been studied for decades and is crucial for estimating black hole mas

Ekin Akyürek, Mehul Damani, Adam Zweiger, Linlu Qiu

Language models (LMs) have shown impressive performance on tasks within their training distribution, but often struggle with structurally novel tasks even when given a small number of in-context task examples. We investigate the effectiveness of test-time training (TTT) -- temporarily updating model parameters during inference using a loss derived from input

Daniel McGinnis, Nikola Sadovek

We solve a long-standing open problem posed by Goodman \& Pollack in 1988 by establishing a necessary and sufficient condition for a family of convex sets in $\mathbb{R}^d$ to admit a $k$-transversal for any $0 \le k \le d-1$. This result is a common generalization of Helly's theorem ($k=0$) and the Goodman-Pollack-Wenger theorem ($k=d-1$). Additionally, we

Bo Yang, Qingping Yang, Yingwei Ma, Runtao Liu

The evaluation of mathematical reasoning capabilities is essential for advancing Artificial General Intelligence (AGI). While Large Language Models (LLMs) have shown impressive performance in solving mathematical problems, existing benchmarks such as GSM8K and MATH present limitations, including narrow problem definitions with specific numbers and reliance o

Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin

We propose a novel fine-tuning method to achieve multi-operator learning through training a distributed neural operator with diverse function data and then zero-shot fine-tuning the neural network using physics-informed losses for downstream tasks. Operator learning effectively approximates solution operators for PDEs and various PDE-related problems, yet it

Sumeth Yuenyong, Kobkrit Viriyayudhakorn, Apivadee Piyatumrong, Jillaphat Jaroenkantasima

OpenThaiGPT 1.5 is an advanced Thai language chat model based on Qwen v2.5, finetuned on over 2,000,000 Thai instruction pairs. This report provides an engineering perspective on the model's development, capabilities, and performance. We discuss the model's architecture, training process, and key features, including multi-turn conversation support, Retrieval

Chaitanya Malaviya, Joseph Chee Chang, Dan Roth, Mohit Iyyer

Language model users often issue queries that lack specification, where the context under which a query was issued -- such as the user's identity, the query's intent, and the criteria for a response to be useful -- is not explicit. For instance, a good response to a subjective query like "What book should I read next?" would depend on the user's preferences,

Seyed Danial Hashemi, Sunil Mittal

Generating coherent optical frequency combs in micro-ring resonators with Kerr nonlinearity has remarkably advanced the fundamental understanding and applications of temporal dissipative solitons. However, the spectrum of such soliton combs is restricted to the conventional definition of combs as phase-locked, equidistant lines in frequency. Here, we introdu

Taha El Hajji, Antti Lehikoinen, Anouar Belahcen

Circulating currents occurring in windings of electric machines received rising interest recent years. Circulating currents represent unwanted currents flowing between parallel-connected conductors. This phenomenon is due to various reasons such as asymmetries in the winding and differences in electric potential between parallel-connected conductors. This ef

Valery V. Ryzhikov

In 1949, V.A. Rokhlin introduced new invariants for measure-preserving transformations, called k-fold mixing. Does mixing imply k-fold mixing? -- this problem remains open. We recall shortly some results and discuss related problems.

Agnish Kumar Behera, Alexandra Lamtyugina, Aditya Nandy, Daiki Goto

Generative diffusion models have emerged as powerful tools for sampling high-dimensional distributions, yet they typically rely on white gaussian noise and noise schedules to destroy and reconstruct information. Here, we demonstrate that driving the generative process out of equilibrium using active, temporally correlated noise sources fundamentally alters t

Yoad Tewel, Rinon Gal, Dvir Samuel, Yuval Atzmon

Adding Object into images based on text instructions is a challenging task in semantic image editing, requiring a balance between preserving the original scene and seamlessly integrating the new object in a fitting location. Despite extensive efforts, existing models often struggle with this balance, particularly with finding a natural location for adding an

Tom Sander, Pierre Fernandez, Alain Durmus, Teddy Furon

Image watermarking methods are not tailored to handle small watermarked areas. This restricts applications in real-world scenarios where parts of the image may come from different sources or have been edited. We introduce a deep-learning model for localized image watermarking, dubbed the Watermark Anything Model (WAM). The WAM embedder imperceptibly modifies

Simon Munyan, Sina Ahadi, Binghao Guo, Arman Rashidi

The quantum Wigner crystal is a many-body state where Coulombic repulsion quenches the kinetic energy of electrons, causing them to crystallize into a lattice. Experimental realization of a quantum Wigner crystal at zero magnetic field has been a long-sought goal. Here, we report on the experimental evidence of a Wigner solid in ultra-thin films of cadmium a

Harsh Rangwani

The datasets used for Deep Neural Network training (e.g., ImageNet, MSCOCO, etc.) are often manually balanced across categories (classes) to facilitate learning of all the categories. This curation process is often expensive and requires throwing away precious annotated data to balance the frequency across classes. This is because the distribution of data in

Botao Yu, Frazier N. Baker, Ziru Chen, Garrett Herb

To enhance large language models (LLMs) for chemistry problem solving, several LLM-based agents augmented with tools have been proposed, such as ChemCrow and Coscientist. However, their evaluations are narrow in scope, leaving a large gap in understanding the benefits of tools across diverse chemistry tasks. To bridge this gap, we develop ChemToolAgent, an e

Luigi Zallio, Giovanni Rosotti, Benoît Tabone, Leonardo Testi

There is still much uncertainty around the mechanism that rules the accretion of proto-planetary disks. In the last years, Magnetohydrondynamic (MHD) wind-driven accretion has been proposed as a valid alternative to the more conventional viscous accretion. In particular, winds have been shown to reproduce the observed correlation between the mass of the disk

Marcel Gievers, Richard Schmidt, Jan von Delft, Fabian B. Kugler

The Fermi-edge singularity in x-ray absorption spectra of metals is a paradigmatic case of a logarithmically divergent perturbation series. Prior work has thoroughly analyzed the leading logarithmic terms. Here, we investigate the perturbation theory beyond leading logarithms and formulate self-consistent equations to incorporate all leading and next-to-lead

Shadman Tajwar Shahid

This paper introduces an algorithm to generate a 3D extruder path, combining classic planar and non-planar layers to enhance the surface quality and accuracy of complex 3D printed parts. Material extrusion 3D printing, due to its layer-by-layer construction method, produces parts with a discretization effect commonly referred to as the "staircase" effect, pa

Matheus Simão, Fabiano Prado, Omar Abdul Wahab, Anderson Avila

With the widespread of digital environments, reliable authentication and continuous access control has become crucial. It can minimize cyber attacks and prevent frauds, specially those associated with identity theft. A particular interest lies on keystroke dynamics (KD), which refers to the task of recognizing individuals' identity based on their unique typi

Yunhao Luo, Yilun Du

Large video models, pretrained on massive amounts of Internet video, provide a rich source of physical knowledge about the dynamics and motions of objects and tasks. However, video models are not grounded in the embodiment of an agent, and do not describe how to actuate the world to reach the visual states depicted in a video. To tackle this problem, current

Bohdan Grzadkowski, Anna Socha

A quantum theory of massive Abelian vector bosons with non-minimal couplings to gravity has been studied within an evolving, isotropic, and homogeneous gravitational background. The vectors may play a role of dark matter if stabilizing $\mathbb{Z}_2$ symmetry is imposed. In order to construct a gauge invariant theory of massive vectors that couple to the Ric

Jason Aebischer, Tejhas Kapoor, Jacky Kumar

Sterile neutrinos are well-motivated beyond the Standard Model (BSM) particles. The Standard Model Effective Field Theory (SMEFT) augmented with these new fields is known as the $\nu$SMEFT. We present the first code for solving the renormalization group equations (RGEs) of the $\nu$SMEFT in an automated way. For this purpose, we have implemented the $\nu$SME

Alexander Douglas, Vassilios Kaxiras, Lin Su, Michal Szurek

Entanglement can improve the measurement precision of quantum sensors beyond the shot noise limit. Neutral atoms, the basis of some of the most precise and accurate optical clocks and interferometers, do not naturally exhibit all-to-all interactions that are traditionally used to generate such entangled states. Instead, we take advantage of the magnetic dipo

Pierre Colonna D'Istria, Abdulrahman Altahhan

In this paper, we introduce TreeCoders, a novel family of transformer trees. We moved away from traditional linear transformers to complete k-ary trees. Transformer blocks serve as nodes, and generic classifiers learn to select the best child and route the sequence of tokens to a specific leaf. The selectors, moved outside the transformer blocks, allow for t

Fuwei Li

This paper presents a novel feature selection method leveraging the Wasserstein distance to improve feature selection in machine learning. Unlike traditional methods based on correlation or Kullback-Leibler (KL) divergence, our approach uses the Wasserstein distance to assess feature similarity, inherently capturing class relationships and making it robust t

Ersoy Şaşıoğlu, Paul Bodewei, Nicki F. Hinsche, Ingrid Mertig

Spin-gapless semiconductors (SGSs) are a promising class of materials for spintronic applications, enabling functions beyond conventional electronics. This study introduces a novel design for multifunctional spintronic field-effect transistors (FETs) using SGSs and/or spin-gapped metals (SGMs) as source and drain electrodes. These devices operate similarly t

Yue Yao, Grant Iraci, Cheng-En Chuang, Stephanie Balzer

Many of today's message-passing systems not only require messages to be exchanged in a certain order but also to happen at a certain \emph{time} or within a certain \emph{time window}. Such correctness conditions are particularly prominent in Internet of Things (IoT) and real-time systems applications, which interface with hardware devices that come with inh

R. Vishnupriya, R. Rajkumar

We introduce a hypergraph matrix, named the unified matrix, and use it to represent the hypergraph as a graph. We show that the unified matrix of a hypergraph is identical to the adjacency matrix of the associated graph. This enables us to use the spectrum of the unified matrix of a hypergraph as a tool to connect the structural properties of the hypergraph

Madeline Brumley, Joe Kwon, David Krueger, Dmitrii Krasheninnikov

A key objective of interpretability research on large language models (LLMs) is to develop methods for robustly steering models toward desired behaviors. To this end, two distinct approaches to interpretability -- ``bottom-up" and ``top-down" -- have been presented, but there has been little quantitative comparison between them. We present a case study compa

Tiantian Mao, Gilles Stupfler, Fan Yang

We study a general risk measure called the generalized shortfall risk measure, which was first introduced in Mao and Cai (2018). It is proposed under the rank-dependent expected utility framework, or equivalently induced from the cumulative prospect theory. This risk measure can be flexibly designed to capture the decision maker's behavior toward risks and w

Yuka Ikarashi, Kevin Qian, Samir Droubi, Alex Reinking

User-schedulable languages (USLs) help programmers productively optimize programs by providing safe means of transforming them. Current USLs are designed to give programmers exactly the control they want, while automating all other concerns. However, there is no universal answer for what performance-conscious programmers want to control, how they want to con

Camille Labourie, Yana Teplitskaya

Quasiminimal sets are sets for which a pertubation can decrease the area but only in a controlled manner. We prove that in dimensions $2$ and $3$, such sets separate a locally finite family of local John domains. Reciprocally, we show that this property is a sufficient for quasiminimality. In addition, we show that quasiminimal sets locally separate the spac

S. W. Hancock, N. Tripathi, M. S. Le, A. Goffin

We measure the change in transverse orbital angular momentum (tOAM) per photon, delta L_y, applied to an optical pulse by a pure amplitude perturbation. The results are in excellent agreement with calculations and simulations of the spatiotemporal torque based on our tOAM theory [Phys. Rev. Lett. 127, 193901 (2021)]. The crucial factor in determining delta L

Wei Dai, Gangqiang Liu, Vidul Joshi, Alessandro Miano

Josephson element-based parametric amplifiers (JPAs) typically require rf pump power that is several orders of magnitude stronger than the maximum signal power they can handle. The low power efficiency and strong pump leakage towards signal circuitry could be critical concerns in application. In this work, we discuss how to optimize the pump coupling scheme