November 2024 arXiv papers — page 161

Ilya Belopolski, Ryota Watanabe, Yuki Sato, Ryutaro Yoshimi

Quantum materials governed by emergent topological fermions have become a cornerstone of physics. Dirac fermions in graphene form the basis for moir\'e quantum matter, and Dirac fermions in magnetic topological insulators enabled the discovery of the quantum anomalous Hall effect. In contrast, there are few materials whose electromagnetic response is dominat

Luca Broggi

In this Letter, we present a new formulation of loss cone theory as a reaction-diffusion system, which accounts for loss cone events through a sink term and can be orbit-averaged. It can recover the standard approach based on boundary conditions, and is derived from a simple physical model that overcomes many of the classical theoretical constraints. We test

F. Dux, M. Millon, C. Lemon, T. Schmidt

We report the discovery of the first example of an Einstein zig-zag lens, an extremely rare lensing configuration. In this system, J1721+8842, six images of the same background quasar are formed by two intervening galaxies, one at redshift $z_1 = 0.184$ and a second one at $z_2 = 1.885$. Two out of the six multiple images are deflected in opposite directions

Ting-Yi Lu, Charlotte A. Mason, Andrei Mesinger, David Prelogović

Ionized bubble sizes during reionization trace physical properties of the first galaxies. JWST's ability to spectroscopically confirm and measure Lyman-alpha (Ly$\alpha$) emission in sub-L* galaxies opens the door to mapping ionized bubbles in 3D. However, existing Lya-based bubble measurement strategies rely on constraints from single galaxies, which are li

Orion Ning, Christopher Dessert, Vi Hong, Benjamin R. Safdi

Low mass axion-like particles could be produced in abundance within the cores of hot, compact magnetic white dwarf (MWD) stars from electron bremsstrahlung and converted to detectable X-rays in the strong magnetic fields surrounding these systems. In this work, we constrain the existence of such axions from two dedicated Chandra X-ray observations of $\sim$4

Jyotismita Adhikary, Luis A. Anchordoqui, Akitaka Ariga, Tomoko Ariga

The recent direct detection of neutrinos at the LHC has opened a new window on high-energy particle physics and highlighted the potential of forward physics for groundbreaking discoveries. In the last year, the physics case for forward physics has continued to grow, and there has been extensive work on defining the Forward Physics Facility and its experiment

Boran Zhou, Ya-Hui Zhang

The recent experimental observation of quantum anomalous Hall (QAH) effects in the rhombohedrally stacked pentalayer graphene has motivated theoretical discussions on the possibility of quantum anomalous Hall crystal (QAHC), a topological version of Wigner crystal. Conventional topological Wigner crystals typically have one electron per unit cell. In this wo

Xu-Xiang Li, Xiaochuan Lu, Zhengkang Zhang

We derive universal formulae for integrating out heavy degrees of freedom in scalar field theories up to one-loop level in terms of covariant quantities associated with the geometry of the field manifold. The universal matching results can be readily applied to phenomenologically interesting extensions of the Standard Model, as we demonstrate using a singlet

Chandramouli Chowdhury, George Doran, Arthur Lipstein, Ricardo Monteiro

Self-dual Yang-Mills and Einstein gravity in Euclidean AdS$_4$ are useful toy models because they can be described by simple scalar Lagrangians exhibiting a new manifestation of the colour/kinematics duality, as recently shown by two of the authors. In this paper, we clarify how the self-dual sectors fit into the full theories. In particular, we explicitly c

Yuxuan Zhang, Maissam Barkeshli

Fractional Chern insulators (FCI) with crystalline symmetry possess topological invariants that fundamentally have no analog in continuum fractional quantum Hall (FQH) states. Here we demonstrate through numerical calculations on model wave functions that FCIs possess a fractionally quantized electric polarization, $\vec{\mathscr{P}}_{\text{o}}$, where $\tex

Mael Cavan-Piton, Diego Guadagnoli, Axel Iohner, Diego Martinez Santos

Two-body decays like $K \to \pi a$ rank among the most constraining collider probes for new, low-mass, feebly interacting pseudoscalar particles $a$. We explore an alternative class of kaon decay modes, specifically three-body decays to $\pi \pi a$ or $\mu \mu a$. The former occur at tree level, while the latter is loop-suppressed yet accidentally finite. Th

Gregory Bentsen, Bill Fefferman, Soumik Ghosh, Michael J. Gullans

While many statistical properties of deep random quantum circuits can be deduced, often rigorously and other times heuristically, by an approximation to global Haar-random unitaries, the statistics of constant-depth random quantum circuits are generally less well-understood due to a lack of amenable tools and techniques. We circumvent this barrier by conside

Jeongsoo Park, Andrew Owens

One of the key challenges of detecting AI-generated images is spotting images that have been created by previously unseen generative models. We argue that the limited diversity of the training data is a major obstacle to addressing this problem, and we propose a new dataset that is significantly larger and more diverse than prior work. As part of creating th

Hao Phung, Quan Dao, Trung Dao, Hoang Phan

We introduce a novel state-space architecture for diffusion models, effectively harnessing spatial and frequency information to enhance the inductive bias towards local features in input images for image generation tasks. While state-space networks, including Mamba, a revolutionary advancement in recurrent neural networks, typically scan input sequences from

Samantha N. Hasler, L. C. Mayorga, William M. Grundy, Amy A. Simon

We present flux measurements of Uranus observed at phase angles of 43.9{\deg}, 44.0{\deg}, and 52.4{\deg} by the Multispectral Visible Imaging Camera (MVIC) on the New Horizons spacecraft during 2023, 2010, and 2019, respectively. New Horizons imaged Uranus at a distance of about 24-70 AU (2023) in four color filters, with bandpasses of 400-550 nm, 540-700 n

Jeremy Ahrens Huang, Young Kun Ko, Chunhao Wang

We show a linear-size reduction from gap Max-2-Lin(2) (a generalization of the gap $\mathrm{Max}$-$\mathrm{Cut}$ problem) to $\gamma\text{-}\mathrm{CVP}_p$ for $\gamma = \mathrm{O}(1)$ and finite $p\geq 1$, as well as a no-go theorem against poly-sized non-adaptive quantum reductions from $k$-SAT to $\mathrm{CVP}_2$. This implies three headline results: (i)

Ziyao Fu, Yulin Peng, Yuchong Zhang

We show that all totally positive formal power series with integer coefficients and constant term $1$ are precisely the rank-generating functions of Schur-positive upho posets, thereby resolving the main conjecture proposed by Gao, Guo, Seetharaman, and Seidel. To achieve this, we construct a bijection between finitary colored upho posets and atomic, left-ca

Matteo Fadel, Noah Roux, Manuel Gessner

As one of the main pillars of quantum technologies, quantum metrology aims to improve measurement precision using techniques from quantum information. The two main strategies to achieve this are the preparation of nonclassical states and the design of optimized measurement observables. We discuss precision limits and optimal strategies in quantum metrology a

Bogdan A. Dobrescu

Scalar particles that couple to two up quarks may be produced at the LHC even if they are ultraheavy, in the $8-10$ TeV mass range. A renormalizable theory that includes a diquark particle of this type ($S_{uu}$), two vectorlike quarks ($\chi_1, \chi_2$), and a gauge-singlet pseudoscalar, predicts LHC signals involving four or more jets of very high $p_T$. T

Felix Huber, Kevin Thompson, Ojas Parekh, Sevag Gharibian

Quantum Max Cut (QMC), also known as the quantum anti-ferromagnetic Heisenberg model, is a QMA-complete problem relevant to quantum many-body physics and computer science. Semidefinite programming relaxations have been fruitful in designing theoretical approximation algorithms for QMC, but are computationally expensive for systems beyond tens of qubits. We g

Yurii Kolomoitsev, Sergey Tikhonov

We obtain Marcinkiewicz--ygmund (MZ) inequalities in various Banach and quasi-Banach spaces under minimal assumptions on the structural properties of these spaces. Our main results show that the Bernstein inequality in a general quasi-Banach function lattice $X$ implies Marcinkiewicz-Zygmund type estimates in $X$. We present a general approach to obtain MZ i

Scott E. Friedman, Noam Benkler, Drisana Mosaphir, Jeffrey Rye

Large language models (LLMs) generate diverse, situated, persuasive texts from a plurality of potential perspectives, influenced heavily by their prompts and training data. As part of LLM adoption, we seek to characterize - and ideally, manage - the socio-cultural values that they express, for reasons of safety, accuracy, inclusion, and cultural fidelity. We

Daniel P. Jeong, Saurabh Garg, Zachary C. Lipton, Michael Oberst

Several recent works seek to develop foundation models specifically for medical applications, adapting general-purpose large language models (LLMs) and vision-language models (VLMs) via continued pretraining on publicly available biomedical corpora. These works typically claim that such domain-adaptive pretraining (DAPT) improves performance on downstream me

Muhammad Akram, Fan Yang, Turan Birol, Onur Erten

Experimental detection of antiferromagnetic order in two-dimensional materials is a challenging task due to the absence of net dipole moments. Identifying multi-domain antiferromagnetic textures via the current techniques is even more difficult. In order to address this challenge, we investigate the higher order multipole moments in twisted bilayer MnPSe$_3$

Nicolás Álvarez, Verónica Becher, Eda Cesaratto, Martín Mereb

We define Poisson genericity for infinite sequences in any finite or countable alphabet with an invariant exponentially-mixing probability measure. A sequence is Poisson generic if the number of occurrences of blocks of symbols asymptotically follows a Poisson law as the block length increases. We prove that almost all sequences are Poisson generic. Our resu

Eshan Chattopadhyay, Mohit Gurumukhani, Noam Ringach, Rocco Servedio

We study the tasks of deterministically condensing and extracting from Online Non-Oblivious Symbol Fixing (oNOSF) sources, a natural model of defective randomness where extraction is impossible in many parameter regimes [AORSV, EUROCRYPT'20]. A $(g,\ell)$-oNOSF source is a sequence of $\ell$ blocks where at least $g$ blocks are good (independent, with min-en

Arunabh Srivastava, Thomas Jacob Maranzatto, Sennur Ulukus

We consider a gossiping network, where a source node sends updates to a network of $n$ gossiping nodes. Meanwhile, the connectivity topology of the gossiping network changes over time, among a finite number of connectivity ''states,'' such as the fully connected graph, the ring graph, the grid graph, etc. The transition of the connectivity graph among the po

C. A. Condos, J. R. Pratt, J. Manley, A. R. Agrawal

We explore a new class of chipscale torsion pendula formed by Si$_3$N$_4$ nanoribbon suspensions. Owing to their unique hierarchy of gravitational, tensile, and elastic stiffness, the devices exhibit damping rates of $\sim 10\;\mu$Hz and parametric gravity sensitivities near that of an ideal pendulum. The suspension nonlinearity can also be used to cancel th

Shreya Gummadi, Mateus V. Gasparino, Deepak Vasisht, Girish Chowdhary

Centralized learning requires data to be aggregated at a central server, which poses significant challenges in terms of data privacy and bandwidth consumption. Federated learning presents a compelling alternative, however, vanilla federated learning methods deployed in robotics aim to learn a single global model across robots that works ideally for all. But

Masahiro Ono

Background: Fluorescent Timer proteins, which display time-dependent changes in their emission spectra, are invaluable for analyzing the temporal dynamics of cellular events at the single-cell level. We previously developed the Timer-of-cell-kinetics-and-activity (Tocky) tools, utilizing a specific Timer protein, Fast-FT, to monitor temporal changes in cellu

Gordana Dodig-Crnkovic, Gianfranco Basti, Tobias Holstein

As AI systems increasingly operate with autonomy and adaptability, the traditional boundaries of moral responsibility in techno-social systems are being challenged. This paper explores the evolving discourse on the delegation of responsibilities to intelligent autonomous agents and the ethical implications of such practices. Synthesizing recent developments

Eduardo García-Portugués, Andrea Meilán-Vila

A kernel density estimator for data on the polysphere $\mathbb{S}^{d_1}\times\cdots\times\mathbb{S}^{d_r}$, with $r,d_1,\ldots,d_r\geq 1$, is presented in this paper. We derive the main asymptotic properties of the estimator, including mean square error, normality, and optimal bandwidths. We address the kernel theory of the estimator beyond the von Mises-Fis

Alessio Figalli, André Guerra, Sunghan Kim, Henrik Shahgholian

In this short expository note, we present a selection of classic and recent ideas in free boundary theory, with a focus on the vectorial case, referred to here as constraint maps. The note includes a brief historical perspective and highlights the latest heuristic-level results.

Niklas Schmidinger, Lisa Schneckenreiter, Philipp Seidl, Johannes Schimunek

Language models for biological and chemical sequences enable crucial applications such as drug discovery, protein engineering, and precision medicine. Currently, these language models are predominantly based on Transformer architectures. While Transformers have yielded impressive results, their quadratic runtime dependency on the sequence length complicates

Archiki Prasad, Weizhe Yuan, Richard Yuanzhe Pang, Jing Xu

Self-alignment, whereby models learn to improve themselves without human annotation, is a rapidly growing research area. However, existing techniques often fail to improve complex reasoning tasks due to the difficulty of assigning correct rewards. An orthogonal approach that is known to improve correctness is self-consistency, a method applied at inference t

Ahmed Abdeljawad, Thomas Dittrich

In this work, we consider the approximation capabilities of shallow neural networks in weighted Sobolev spaces for functions in the spectral Barron space. The existing literature already covers several cases, in which the spectral Barron space can be approximated well, i.e., without curse of dimensionality, by shallow networks and several different classes o

Anjasha Gangopadhyay

Interacting supernovae provide key insights into the mass-loss processes of massive stars and their circumstellar environments. By analyzing their photometric and spectroscopic properties, we can study the complex interactions between ejected material and circumstellar material (CSM). This paper highlights the diversity of interacting SNe, including Types II

Joseph Balderas, Dong Chen, Yanbo Huang, Li Wang

Crop production management is essential for optimizing yield and minimizing a field's environmental impact to crop fields, yet it remains challenging due to the complex and stochastic processes involved. Recently, researchers have turned to machine learning to address these complexities. Specifically, reinforcement learning (RL), a cutting-edge approach desi

Guan Zhe Hong, Nishanth Dikkala, Enming Luo, Cyrus Rashtchian

Due to the size and complexity of modern large language models (LLMs), it has proven challenging to uncover the underlying mechanisms that models use to solve reasoning problems. For instance, is their reasoning for a specific problem localized to certain parts of the network? Do they break down the reasoning problem into modular components that are then exe

Amanda Xu, Abtin Molavi, Swamit Tannu, Aws Albarghouthi

Optimizing quantum circuits is critical: the number of quantum operations needs to be minimized for a successful evaluation of a circuit on a quantum processor. In this paper we unify two disparate ideas for optimizing quantum circuits, rewrite rules, which are fast standard optimizer passes, and unitary synthesis, which is slow, requiring a search through t

Peixin Zhu, Lisa J. Kewley, Ralph Sutherland

Gas metallicity, ionization parameter, and gas pressure can affect the observed ratios of specific strong emission lines within galaxies. While the theoretical strong lines diagnostics for gas metallicity, ionization parameters, and gas pressure in star-forming regions are well-established, theoretical diagnostics for active galactic nuclei (AGNs) narrow lin

Anne V. Shepler, Sarah Witherspoon

Alexander-Whitney and Eilenberg-Zilber maps traditionally convert between the tensor product of standard resolutions and the standard resolution of a tensor product of algebras. We examine Alexander-Whitney and Eilenberg-Zilber maps for twisted tensor products, which include skew group algebras, smash products of Hopf algebras, Ore extensions, and universal

Ted Thorbeck, Alexander McDonald, O. Lanes, John Blair

Leakage, the occupation of any state not used in the computation, is one of the of the most devastating errors in quantum error correction. Transmons, the most common superconducting qubits, are weakly anharmonic multilevel systems, and are thus prone to this type of error. Here we demonstrate a device which reduces the lifetimes of the leakage states in the

Iolo Jones

We introduce novel estimators for computing the curvature, tangent spaces, and dimension of data from manifolds, using tools from diffusion geometry. Although classical Riemannian geometry is a rich source of inspiration for geometric data analysis and machine learning, it has historically been hard to implement these methods in a way that performs well stat

Saif Farhat, Guillem Sole-Mari, Diogo Bolster

We study the pore-scale transport of a conservative scalar forming an advancing mixing front, which can be re-interpreted to predict instantaneous mixing-limited bimolecular reactions. We investigate this using a set of two-dimensional, high-resolution numerical simulations within a poly-disperse granular porous medium, covering a wide range of Peclet number

Florian Wolf, Nicolò Botteghi, Urban Fasel, Andrea Manzoni

Effectively controlling systems governed by Partial Differential Equations (PDEs) is crucial in several fields of Applied Sciences and Engineering. These systems usually yield significant challenges to conventional control schemes due to their nonlinear dynamics, partial observability, high-dimensionality once discretized, distributed nature, and the require

Maya Varma, Jean-Benoit Delbrouck, Zhihong Chen, Akshay Chaudhari

Fine-tuned vision-language models (VLMs) often capture spurious correlations between image features and textual attributes, resulting in degraded zero-shot performance at test time. Existing approaches for addressing spurious correlations (i) primarily operate at the global image-level rather than intervening directly on fine-grained image features and (ii)

N Ramadas, Arul Lakshminarayan

The classification of multipartite entanglement is essential as it serves as a resource for various quantum information processing tasks. This study concerns a particular class of highly entangled multipartite states, the so-called absolutely maximally entangled (AME) states. These are characterized by maximal entanglement across all possible bipartitions. I

Lei Cheng, Ming Zhang, Ryun Young Kwon, David Lario

Solar energetic particle (SEP) events are associated with coronal mass ejections (CMEs) and/or solar flares. SEPs travel through the corona and interplanetary space to reach Earth, posing a radiation hazard to spacecraft and astronauts working in space and the electronics on spacecraft. Due to the distinct magnetic field configuration and solar eruption kine

Sahand Hassanizorgabad

Financial markets are nonlinear with complexity, where different types of assets are traded between buyers and sellers, each having a view to maximize their Return on Investment (ROI). Forecasting market trends is a challenging task since various factors like stock-specific news, company profiles, public sentiments, and global economic conditions influence t

Vasudevarao Allu, Raju Biswas, Rajib Mandal

The primary objective of this paper is to establish several sharp versions of improved Bohr inequalities, refined Bohr inequalities, and Bohr-Rogosinski inequalities for the class of $K$-quasiconformal sense-preserving harmonic mappings $f=h+\overline{g}$ in the unit disk $\Bbb{D}:=\{z\in\mathbb{C}: |z|<1\}$, where $f$ and $g$ are analytic functions in $\mat

Nicholas Deas, Kathleen McKeown

Global partisan hostility and polarization has increased, and this polarization is heightened around presidential elections. Models capable of generating accurate summaries of diverse perspectives can help reduce such polarization by exposing users to alternative perspectives. In this work, we introduce a novel dataset and task for independently summarizing

Kornelija Passek-K.

We discuss deeply virtual meson production (DVMP), focusing on the role of higher-twist contributions in the description of deeply virtual pseudoscalar mesons at experimentally accessible energies. The standard collinear approach at the lowest twist does not adequately describe deeply virtual $\pi_0$ production. By incorporating twist-2 transversity generali

J. Ma, C. Ginski, R. Tazaki, C. Dominik

PDS 70 is a unique system as it hosts a protoplanetary disk with two confirmed forming planets, making it an ideal target for characterizing dust in such disks. We present new high-contrast polarimetric differential imaging of PDS 70 using the $N\_R$ filter on SPHERE/ZIMPOL, combined with archival VLT/SPHERE data across five wavelengths ($N\_R$, $VBB$, $J$,

Guillermo Villate-Castillo, Javier Del Ser, Borja Sanz

Content moderation typically combines the efforts of human moderators and machine learning models. However, these systems often rely on data where significant disagreement occurs during moderation, reflecting the subjective nature of toxicity perception. Rather than dismissing this disagreement as noise, we interpret it as a valuable signal that highlights t

Vanja Marić, Florent Ferro, Maurizio Fagotti

We consider time evolution of order parameters and entanglement asymmetries in the ferromagnetic phase of the transverse-field Ising chain. One side of the system is prepared in a ferromagnetic ground state and the other side either in equilibrium at higher temperature or out of equilibrium. We focus on the disorder-order interface in which the order paramet

Nicole Tianjiao Yang, Tomoyuki Ichiba

The strong relative arbitrage problem in Stochastic Portfolio Theory seeks an investment strategy that almost surely outperforms a benchmark portfolio at the end of a given time horizon. The highest relative return in relative arbitrage opportunities is characterized by the smallest nonnegative continuous solution of a Cauchy problem for a partial differenti

Tim Eifler, Xiao Fang, Elisabeth Krause, Christopher M. Hirata

We explore synergies between the Nancy Grace Roman Space Telescope High Latitude Wide Area Survey (HLWAS) and CMB experiments, specifically Simons Observatory (SO) and CMB-Stage4 (S4). Our simulated analyses include weak lensing, photometric galaxy clustering, CMB lensing, thermal SZ, and cross-correlations between these probes. While we assume the nominal 1

Tom McClain

In this paper, I present a novel, purely differential geometric approach to the quantization of scalar fields, with a special focus on the familiar case of Minkowski spacetimes. This approach is based on using the natural geometric structures of polysymplectic Hamiltonian field theory to produce an analog of the Kostant-Souriau prequantization map familiar f

David Miloschewsky, Supartha Podder

Aaronson, Bouland, Fitzsimons and Lee introduced the complexity class PDQP (which was original labeled naCQP), an alteration of BQP enhanced with the ability to obtain non-collapsing measurements, samples of quantum states without collapsing them. Although PDQP contains SZK, it still requires $\Omega(N^{1/4})$ queries to solve unstructured search. We formula

Utsav Dewan, Swagato K. Ray

For $f \in \mathscr{S}^2(\mathcal S)_{o}$, the collection of radial $L^2$-Schwartz class functions on Damek--Ricci spaces $\mathcal S$, we consider the Schr\"odinger maximal function, \begin{equation*} S^* f(x):= \displaystyle\sup_{0<t<4/Q^2} \left|S_tf(x)\right|\:,\:\:\:\:\:\:x\in\mathcal S\:, \end{equation*} corresponding to the Laplace--Beltrami operator

Jose González-Abad, José Manuel Gutiérrez

Deep Learning (DL) has shown promise for downscaling global climate change projections under different approaches, including Perfect Prognosis (PP) and Regional Climate Model (RCM) emulation. Unlike emulators, PP downscaling models are trained on observational data, so it remains an open question whether they can plausibly extrapolate unseen conditions and c

Alyssa R. Pfadt-Trilling, Marie-Odile P. Fortier

Effective climate action depends on dismantling the assumptions and oversimplifications that have become the basis of climate policy. The assumption that greenhouse gases (GHG) are fungible and the use of single-point values in normalizing GHG species to CO2-equivalents can propagate inaccuracies in carbon accounting and have already led to failures of carbo

Yingyao Zhou, Natasha Devroye

We focus on designing error-correcting codes for the symmetric Gaussian broadcast channel with feedback. Feedback not only expands the capacity region of the broadcast channel but also enhances transmission reliability. In this work, we study the construction of learned finite blocklength codes for broadcast channels with feedback. Learned error-correcting c

Hoang-Viet Nguyen, Trung-Thuy Kieu, Duc-Trong Luong, Hoang-Long Ngo

This paper concerns the numerical approximation for the invariant distribution of Markovian switching L\'evy-driven stochastic differential equations. By combining the tamed-adaptive Euler-Maruyama scheme with the Multi-level Monte Carlo method, we propose an approximation scheme that can be applied to stochastic differential equations with super-linear grow

Christopher Vairogs, Samihr Hermes, Felix Leditzky

We study the task of localizing multipartite entanglement in pure quantum states onto a subsystem by measuring the remaining systems. To this end, we fix a multipartite entanglement measure and consider two quantities: the multipartite entanglement of assistance (MEA), defined as the entanglement measure averaged over the post-measurement states and maximize

Aniket Deroy, Subhankar Maity

Sarcasm detection is a significant challenge in sentiment analysis, particularly due to its nature of conveying opinions where the intended meaning deviates from the literal expression. This challenge is heightened in social media contexts where code-mixing, especially in Dravidian languages, is prevalent. Code-mixing involves the blending of multiple langua

Ke Fan, Jiangning Zhang, Ran Yi, Jingyu Gong

Text-to-motion generation is a crucial task in computer vision, which generates the target 3D motion by the given text. The existing annotated datasets are limited in scale, resulting in most existing methods overfitting to the small datasets and unable to generalize to the motions of the open domain. Some methods attempt to solve the open-vocabulary motion

J S Greaves

When bright solar-system objects are observed by GHz-THz regime telescopes, off-axis signals bounce around locally and re-enter the signal path with a time delay, causing sinusoidal ripples in output spectra. Ripples that are unstable over time are challenging to remove. A typical detection limit for planetary spectral lines is a fraction of order 0.001 of c

Nhi Pham, Michael Schott

By leveraging both texts and images, large vision language models (LVLMs) have shown significant progress in various multi-modal tasks. Nevertheless, these models often suffer from hallucinations, e.g., they exhibit inconsistencies between the visual input and the textual output. To address this, we propose H-POPE, a coarse-to-fine-grained benchmark that sys

Dominik Nowak

We investigate the diffusive scaling of the Lorentz gas in the presence of an external force of mean-field type. In the weak coupling regime and for diffusive time scales, the test particle's law converges to the probability density satisfying the heat equation. The diffusion coefficient of the heat equation is given by the Green-Kubo relation.

Chuhan Li, Ziyao Shangguan, Yilun Zhao, Deyuan Li

Existing benchmarks for evaluating foundation models mainly focus on single-document, text-only tasks. However, they often fail to fully capture the complexity of research workflows, which typically involve interpreting non-textual data and gathering information across multiple documents. To address this gap, we introduce M3SciQA, a multi-modal, multi-docume

Helmut Abels, Andrea Di Primio, Harald Garcke

We consider a diffuse interface model describing a ternary system constituted by a conductive diblock copolymer and a homopolymer acting as solvent. The resulting dynamics is modeled by two Cahn--Hilliard--Oono equations for the copolymer blocks, accounting for long-range interactions; a classical Cahn--Hiliard equation for the homopolymer and the Maxwell eq

Boris Ginzburg

This paper introduces a definition of ideological polarization of an electorate around a particular central point. The definition is flexible about the location or boundaries of the center. Using US survey data, the paper shows how this approach can be used to establish whether polarization is occurring, and to find the position around which it is happening.

Simona Boyadzhiyska, Richard Lang, Allan Lo, Michael Molloy

We study a generalisation of Vizing's theorem, where the goal is to simultaneously colour the edges of graphs $G_1,\dots,G_k$ with few colours. We obtain asymptotically optimal bounds for the required number of colours in terms of the maximum degree $\Delta$, for small values of $k$ and for an infinite sequence of values of $k$. This asymptotically settles a

Luis Ferroni, Jacob P. Matherne, Lorenzo Vecchi

Three decades ago, Stanley and Brenti initiated the study of the Kazhdan--Lusztig--Stanley (KLS) functions, putting on common ground several polynomials appearing in algebraic combinatorics, discrete geometry, and representation theory. In the present paper we develop a theory that parallels the KLS theory. To each kernel in a given poset, we associate a pol

Dat Pham

We give a new proof of a recent result of Tong Liu, which gives a general control on the torsion in the graded pieces of the so-called integral Hodge filtration associated to a crystalline Galois lattice. Our approach is stack-theoretic, and is inspired on the one hand by a result of Gee--Kisin on the shape of mod $p$ crystalline Breuil--Kisin modules, and o

Alioune Diallo, Aicha War, Moustapha Awwalou Diouf, Jordan Samhi

The West African Economic and Monetary Union (WAEMU) states, characterized by widespread smartphone usage, have witnessed banks and financial institutions introducing mobile banking applications (MBAs). These apps empower users to perform transactions such as money transfers, bill payments, and account inquiries anytime, anywhere. However, this proliferation

Miguel A. S. Pinto, João Luís Rosa

In this work, we analyze the Einstein-scalar-Gauss-Bonnet (EsGB) theory of gravity in a cosmological context using the formalism of dynamical systems. We obtain the equations of motion of the theory and introduce an appropriate set of dynamical variables to allow for a direct comparison with the results from General Relativity (GR). We observe that the cosmo

Md. Rawshan Habib, Md Abu Yusuf, W. M. H Nimsara Warnasuriya, Kumar Sunny

In light of its many benefits, home automation systems are one of the subjects that are becoming ever more prevalent. The term "home automation" describes the remote monitoring and management of household equipment. The Internet and its usages are constantly expanding, which means there is a lot of room for remote access, management, and surveillance of thes

Clément Vidal

A long-lived civilization will inevitably have to migrate towards a nearby star as its home star runs out of nuclear fuel. One way to achieve such a migration is by transforming its star into a stellar engine, and to control its motion in the galaxy. We first provide a brief overview of stellar engines and conclude that looking for technosignatures of stella

Valentin Goblot, Kexin Wu, Enrico Di Lucente, Yuchun Zhu

Among all materials, mono-crystalline diamond has one of the highest measured thermal conductivities, with values above 2000 W/m/K at room temperature. This stems from momentum-conserving `normal' phonon-phonon scattering processes dominating over momentum-dissipating `Umklapp' processes, a feature that also suggests diamond as an ideal platform to experimen

Hosein Gholami, Ishfaq Ahmad Rather, Marco Hofmann, Michael Buballa

We determine parameters of the renormalization group-consistent three-flavor color-superconducting Nambu-Jona-Lasinio (NJL) model that are suited to investigate possible compact-star configurations. Our goal is to provide quark-matter equations of state (EoS) that can be used for hadron-quark hybrid-star constructions. To that end, we mainly focus on the par

Mayura Balakrishnan, Rory Bowens, Fernando Cruz Aguirre, Kaeli Hughes

We present the mission concept "Mission to Analyze the UltraViolet universE" (MAUVE), a wide-field spectrometer and imager conceived during the inaugural NASA Astrophysics Mission Design School. MAUVE responds to the 2023 Announcement of Opportunity for Probe-class missions, with a budget cap of \$1 billion, and would hypothetically launch in 2031. However,

Marco Origlia, Marco Secondini

The performance of the information reconciliation phase is crucial for quantum key distribution (QKD). Reverse reconciliation (RR) is typically preferred over direct reconciliation (DR) because it yields higher secure key rates. However, a significant challenge in continuous-variable (CV) QKD with discrete modulations (such as QAM) is that Alice lacks soft i

Simone Betteti, Giacomo Baggio, Francesco Bullo, Sandro Zampieri

The Hopfield model provides a mathematically idealized yet insightful framework for understanding the mechanisms of memory storage and retrieval in the human brain. This model has inspired four decades of extensive research on learning and retrieval dynamics, capacity estimates, and sequential transitions among memories. Notably, the role and impact of exter

Matteo Rinaldi, Anton Bochkarev, Yury Lysogorskiy, Ralf Drautz

The atomic cluster expansion (ACE) efficiently parameterizes complex energy surfaces of pure elements and alloys. Due to the local nature of the many-body basis, ACE is inherently local or semilocal for graph ACE. Here, we employ descriptor-constrained density functional theory for parameterizing ACE with charge or other degrees of freedom, thereby transferi

Sourabh Patil, Gaomin Tang, Wolfgang Belzig

The transition-metal dichalcogenides featuring Ising spin-orbit coupling in so-called Ising superconductors offer a unique system to study the interplay of singlet and triplet superconductivity. The presence of high critical fields, spectral properties such as the mirage gap, and field-tunable charge and spin currents in Ising-superconductor Josephson juncti

Torgeir Aambø

We introduce the notion of a contramodule over a cocommutative coalgebra in a presentably symmetric monoidal $\infty$-category $\mathcal{C}$, and prove a symmetric monoidal $\infty$-categorical version of Positselski's comodule-contramodule correspondence when the coalgebra is coidempotent. This gives a new perspective on, and a new proof of local duality --

Ping Li, Tao Wang, Xinkui Zhao, Xianghua Xu

Video captioning generate a sentence that describes the video content. Existing methods always require a number of captions (\eg, 10 or 20) per video to train the model, which is quite costly. In this work, we explore the possibility of using only one or very few ground-truth sentences, and introduce a new task named few-supervised video captioning. Specific

L. W. K. Goh, I. Ocampo, S. Nesseris, V. Pettorino

We investigate whether neural networks (NNs) can accurately differentiate between growth-rate data of the large-scale structure (LSS) of the Universe simulated via two models: a cosmological constant and $\Lambda$ cold dark matter (CDM) model and a tomographic coupled dark energy (CDE) model. We built an NN classifier and tested its accuracy in distinguishin

Sujit Rao

The sum-of-squares hierarchy of semidefinite programs has become a common tool for algorithm design in theoretical computer science, including problems in quantum information. In this work we study a connection between a Hermitian version of the SoS hierarchy, related to the quantum de Finetti theorem, and geometric quantization of compact K\"ahler manifolds

Kiran Doshi, Marco Bagatella, Stelian Coros

The combination of behavioural cloning and neural networks has driven significant progress in robotic manipulation. As these algorithms may require a large number of demonstrations for each task of interest, they remain fundamentally inefficient in complex scenarios, in which finite datasets can hardly cover the state space. One of the remaining challenges i

Zesheng Liu, Maryam Rahnemoonfar

Understanding spatio-temporal patterns in polar ice layers is essential for tracking changes in ice sheet balance and assessing ice dynamics. While convolutional neural networks are widely used in learning ice layer patterns from raw echogram images captured by airborne snow radar sensors, noise in the echogram images prevents researchers from getting high-q

Muhammad Qasim Elahi, Mahsa Ghasemi, Murat Kocaoglu

Causal knowledge about the relationships among decision variables and a reward variable in a bandit setting can accelerate the learning of an optimal decision. Current works often assume the causal graph is known, which may not always be available a priori. Motivated by this challenge, we focus on the causal bandit problem in scenarios where the underlying c

Xucheng Zhang

We generalize the notion of S-equivalence, previously defined for semistable vector bundles, to points in arbitrary algebraic stacks and use it to describe the identification of points when passing to the moduli space. As applications, we recover the classical S-equivalence by considering the identification of semistable vector bundles in the moduli space, a

Natsuki Katayama, Yoshihiko Susuki

This paper reports a theory of Koopman operators for a class of hybrid dynamical systems with globally asymptotically stable periodic orbits, called hybrid limit-cycling systems. We leverage smooth structures intrinsic to the hybrid dynamical systems, thereby extending the existing theory of Koopman operators for smooth dynamical systems. Rigorous constructi

Moritz Staudinger, Florina Piroi, Andreas Rauber

There are settings in which reproducibility of ranked lists is desirable, such as when extracting a subset of an evolving document corpus for downstream research tasks or in domains such as patent retrieval or in medical systematic reviews, with high reproducibility expectations. However, as global term statistics change when documents change or are added to

Bochen Yang, Kaizhong Deng, Christopher J Peters, George Mylonas

Optical sensing technologies are emerging technologies used in cancer surgeries to ensure the complete removal of cancerous tissue. While point-wise assessment has many potential applications, incorporating automated large area scanning would enable holistic tissue sampling. However, such scanning tasks are challenging due to their long-horizon dependency an

Andrew Thompson, Alexander Sommers, Alicia Russell-Gilbert, Logan Cummins

Predictive maintenance has been used to optimize system repairs in the industrial, medical, and financial domains. This technique relies on the consistent ability to detect and predict anomalies in critical systems. AI models have been trained to detect system faults, improving predictive maintenance efficiency. Typically there is a lack of fault data to tra