August 2024 arXiv papers — page 4

Xuyang Guo, Kirk W. Madison, James L. Booth, Roman V. Krems

Thermal rate coefficients for some atomic collisions have been observed to be remarkably independent of the details of interatomic interactions at short range. This makes these rate coefficients universal functions of the long-range interaction parameters and masses, which was previously exploited to develop a self-defining atomic sensor for ambient pressure

Tailia Malloy, Maria José Ferreira, Fei Fang, Cleotilde Gonzalez

Cosine similarity between two documents can be computed using token embeddings formed by Large Language Models (LLMs) such as GPT-4, and used to categorize those documents across a range of uses. However, these similarities are ultimately dependent on the corpora used to train these LLMs, and may not reflect subjective similarity of individuals or how their

Yeimy J. Rivera, Samuel T. Badman, Michael L. Stevens, Jaye L. Verniero

After leaving the Sun's corona, the solar wind continues to accelerate and cools, but more slowly than expected for a freely expanding adiabatic gas. We use in situ measurements from the Parker Solar Probe and Solar Orbiter spacecrafts to investigate a stream of solar wind as it traverses the inner heliosphere. The observations show heating and accelerat

Melkamu Mersha, Khang Lam, Joseph Wood, Ali AlShami

Artificial intelligence models encounter significant challenges due to their black-box nature, particularly in safety-critical domains such as healthcare, finance, and autonomous vehicles. Explainable Artificial Intelligence (XAI) addresses these challenges by providing explanations for how these models make decisions and predictions, ensuring transparency,

Sudarshan Rajagopalan, Vishal M. Patel

All-Weather Image Restoration (AWIR) under adverse weather conditions is a challenging task due to the presence of different types of degradations. Prior research in this domain relies on extensive training data but lacks the utilization of additional contextual information for restoration guidance. Consequently, the performance of existing methods is limite

Erik Koelink, Pablo Román, Wadim Zudilin

There are several questions one may ask about polynomials $q_m(x)=q_m(x;t)=\sum_{n=0}^mt^mp_n(x)$ attached to a family of orthogonal polynomials $\{p_n(x)\}_{n\ge0}$. In this note we draw attention to the naturalness of this partial-sum deformation and related beautiful structures. In particular, we investigate the location and distribution of zeros of $q_m(

Igor Ermakov, Oleg Lychkovskiy, Boris V. Fine

We numerically investigate the stability of exceptional periodic classical trajectories in rather generic chaotic many-body systems and explore a possible connection between these trajectories and exceptional nonthermal quantum eigenstates known as "quantum many-body scars". The systems considered are chaotic spin chains with short-range interactions

Olivia R. Green, Yiliang Bao, John R. Lawall, Jason J. Gorman

We show that optomechanical pressure sensors with characterized density and thickness can achieve uncertainty as low as 1.1 % via comparison with a secondary pressure standard. The agreement between the secondary standard and our optomechanical sensors is a necessary step towards using optomechanical devices as primary pressure sensors. Our silicon nitride a

Yijian Fan, Zhenbang Yang, Rui Liu, Mingjie Li

Medical report generation is a critical task in healthcare that involves the automatic creation of detailed and accurate descriptions from medical images. Traditionally, this task has been approached as a sequence generation problem, relying on vision-and-language techniques to generate coherent and contextually relevant reports. However, in this paper, we p

Vrindaa Somjit, Joel Davidsson, Yu Jin, Giulia Galli

Recent predictions suggest that oxides, such as MgO and CaO, could serve as hosts of spin defects with long coherence times and thus be promising materials for quantum applications. However, in most cases specific defects have not yet been identified. Here, by using a high-throughput first-principles framework and advanced electronic structure methods, we id

Bart M. P. Jansen, Yosuke Mizutani, Blair D. Sullivan, Ruben F. A. Verhaegh

The NP-hard Odd Cycle Transversal problem asks for a minimum vertex set whose removal from an undirected input graph $G$ breaks all odd cycles, and thereby yields a bipartite graph. The problem is well-known to be fixed-parameter tractable when parameterized by the size $k$ of the desired solution. It also admits a randomized kernelization of polynomial size

M. Shekari Tousi, K. Azizi, H. R. Moshfegh

We study the semileptonic decay of the doubly heavy baryon $ Ξ^{++}_{cc} $ into the singly heavy baryon $ Ξ^+_{c}$ within the three-point QCD sum rule approach in two possible lepton channels. Our analysis includes perturbative as well as nonperturbative condensation contributions up to dimension 5. We evaluate the form factors of this semileptonic decay ent

Mohammadreza Ganji, Anas El Fathi, Chiara Fabris, Dayu Lv

Background and Objective: Diabetes presents a significant challenge to healthcare due to the negative impact of poor blood sugar control on health and associated complications. Computer simulation platforms, notably exemplified by the UVA/Padova Type 1 Diabetes simulator, has emerged as a promising tool for advancing diabetes treatments by simulating patient

Yilin Zhuang, Sibo Cheng, Karthik Duraisamy

Diffusion models have gained attention for their ability to represent complex distributions and incorporate uncertainty, making them ideal for robust predictions in the presence of noisy or incomplete data. In this study, we develop and enhance score-based diffusion models in field reconstruction tasks, where the goal is to estimate complete spatial fields f

Chun Yu Chow, William A. Wheeler, Lucas K. Wagner

We present an advanced $\textit{ab initio}$ quantum Monte Carlo (QMC) calculation of the ground state of undoped CaCuO$_2$. We extend the traditional single-determinant Slater-Jastrow approach to include multi-determinant wave functions, inhomogeneous Jastrow factors, and orbital optimization. Our results demonstrate not only an improvement in the variationa

Abu Ubaida Akash, Ahmed Fahmy, Amine Trabelsi

Stance detection (SD) identifies the text position towards a target, typically labeled as favor, against, or none. We introduce Open-Target Stance Detection (OTSD), the most realistic task where targets are neither seen during training nor provided as input. We evaluate Large Language Models (LLMs) from GPT, Gemini, Llama, and Mistral families, comparing the

Congzhou M Sha, Bailing Ma

We provide code to solve the Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (DGLAP) evolution equations for the nucleon transversity parton distribution functions (PDFs), which encode nucleon transverse spin structure. Though codes are widely available for the evolution of unpolarized and polarized PDFs, there are few codes publicly available for the transversit

Jin Yi Yong, Rudy Geelen, Johann Guilleminot

This paper presents a probabilistic approach to represent and quantify model-form uncertainties in the reduced-order modeling of complex systems using operator inference techniques. Such uncertainties can arise in the selection of an appropriate state-space representation, in the projection step that underlies many reduced-order modeling methods, or as a byp

Hector Gisbert, Antonio Rodríguez-Sánchez, Luiz Vale Silva

Within the Standard Model Effective Field Theory framework, we set indirect constraints on top quark operators that violate baryon number by one unit above the TeV scale. We find that these constraints are typically many orders of magnitude more stringent than the recently derived direct bounds from collider experiments. Therefore, direct observation of bary

Ada Defne Tur, Adel Moumen, Mirco Ravanelli

Large Language Models (LLMs) have shown their ability to improve the performance of speech recognizers by effectively rescoring the n-best hypotheses generated during the beam search process. However, the best way to exploit recent generative instruction-tuned LLMs for hypothesis rescoring is still unclear. This paper proposes a novel method that uses instru

Denis S. Grebenkov

We consider the mixed Steklov-Neumann spectral problem for the modified Helmholtz equation in a bounded domain when the Steklov condition is imposed on a connected subset of the smooth boundary. In order to deduce the asymptotic behavior in the limit when the size of the subset goes to zero, we reformulate the original problem in terms of an integral operato

Sha Lu, Xuecheng Xu, Yuxuan Wu, Haojian Lu

Global localization using onboard perception sensors, such as cameras and LiDARs, is crucial in autonomous driving and robotics applications when GPS signals are unreliable. Most approaches achieve global localization by sequential place recognition (PR) and pose estimation (PE). Some methods train separate models for each task, while others employ a single

E. J. Watkins, N. Peretto, A. J. Rigby, R. J. Smith

Understanding how stars form, evolve and impact molecular clouds is key to understanding why star formation is such an inefficient process globally. In this paper, we use the infrared bright fraction, $f_\text{IRB}$ (the fraction of a given molecular cloud that appears bright against the 8 $μ$m Milky Way background) as a proxy for time evolution to test how

Zeyu Zhang, Nengmin Yi, Shengbo Tan, Ying Cai

Cervical disc herniation (CDH) is a prevalent musculoskeletal disorder that significantly impacts health and requires labor-intensive analysis from experts. Despite advancements in automated detection of medical imaging, two significant challenges hinder the real-world application of these methods. First, the computational complexity and resource demands pre

Fatih Bildirici, Keziban Seckin Codal, Tunc Durmus Medeni

In the realm of Agile software development, precise user story point estimation is crucial for effectual project timeline and resource management. Despite its significance, the method is often marred by issues stemming from cognitive biases, disparities in individual judgment, and hurdles related to both collaboration and competition. In addressing these cha

Sarah G. Kane, Vasily Belokurov, Miles Cranmer, Stephanie Monty

Globular clusters (GCs) are sites of extremely efficient star formation, and recent studies suggest they significantly contributed to the early Milky Way's stellar mass build-up. Although their role has since diminished, GCs' impact on the Galaxy's initial evolution can be traced today by identifying their most chemically unique stars--those with

Dilip Kumar Ghosh, Koustav Mukherjee, Shourya Mukherjee

We investigate the dynamics of the electroweak phase transition within an extended Standard Model framework that includes one real scalar $(Φ)$ and one complex scalar $(S)$, both of which are SM gauge singlets. The global $U(1)$ symmetry is softly broken to a $\mathcal{Z}_3$ symmetry by the $S^3$ term in the scalar potential. After this $U(1)$ symmetry break

Andrey Zhizhin

We develop an approach to study the irreducibility of generic complete intersections in the algebraic torus defined by equations with fixed monomials and fixed linear relations on coefficients. Using our approach we generalize the irreducibility theorems of Khovanskii to fields of arbitrary characteristic. Also we get a combinatorial sufficient conditions fo

Sebastian Echeverria-Alar, Wouter-Jan Rappel

Scroll-wave instabilities in excitable domains are central to life-threatening arrhythmias, yet practical methods to stabilize these dynamics remain limited. Here, we investigate the effects of boundary layer heterogeneities in the spatiotemporal dynamics of a quasi-2D semidiscrete excitable model. We reveal that a novel boundary-driven mechanism suppresses

Mehdi El Bouhaddouti, Ilias Cholis

The merging black hole binaries detected by the LIGO-Virgo-KAGRA (LVK) gravitational-wave observatories, may help us shed light on how such binaries form. In addition, these detections can help us probe the hypothesized primordial black holes, a candidate for the observed abundance of dark matter. In this work, we study the black-hole mass distribution obtai

Chris Hull, Maxwell L Hutt, Ulf Lindström

Linearised gravity has a global symmetry under which the graviton is shifted by a symmetric tensor satisfying a certain flatness condition. There is also a dual symmetry that can be associated with a global shift symmetry of the dual graviton theory. The corresponding conserved charges are shown to satisfy a centrally-extended algebra. We discuss the gauging

Falk Hassler, Ondrej Hulik, David Osten

This article shows that the approach to generalised curvature and torsion pioneered by Polacek and Siegel [1] is a generalisation of Cartan Geometry -- rendering latter natural from the point of view of O(d,d)-generalised geometry. We present this approach in the generalised metric formalism and show that almost all parts of the additional higher generalised

Andrea E. V. Ferrari, Daniel Zhang

In recent work, we demonstrated that a spectral variety for the Berry connection of a 2d $\mathcal{N}=(2,2)$ GLSM with Kähler vacuum moduli space $X$ and abelian flavour symmetry is the support of a sheaf induced by a certain action on the equivariant quantum cohomology of $X$. This action could be quantised to first-order matrix difference equations obeyed

Gordan Krnjaic, Duncan Rocha, Isaac R. Wang

The MUonE experiment aims to extract the hadronic contribution to the muon anomalous magnetic moment from a precise measurement of the muon-electron differential scattering cross section. We show that MUonE can also discover thermal relic dark matter using only its nominal experimental setup. Our search strategy is sensitive to models of dark matter in which

E. Merlin, P. Santini, D. Paris, M. Castellano

We present a set of photometric catalogs primarily aimed at providing the community with a comprehensive database for the study of galaxy populations in the high redshift Universe. The set gathers data from eight JWST NIRCam observational programs, targeting the Abell 2744 (GLASS-JWST, UNCOVER, DDT2756 and GO3990), EGS (CEERS), COSMOS and UDS (PRIMER), and G

Wen Yin, Junya Yoshida

Axion-like particles (ALPs) are known to be produced through axion-photon conversion in the presence of a stationary external magnetic field. Devices such as undulators and wigglers, which are used widely for photon production, e.g., in synchrotron radiation facilities, inherently possess strong magnetic fields, making them potential sources for ALP producti

Jiarui Li, Robert J. Green, Claribel Domínguez, Abraham Levitan

In rare earth nickelates (RENiO$_3$), electron-lattice coupling drives a concurrent metal-to-insulator and bond disproportionation phase transition whose microscopic origin has long been the subject of active debate. Of several proposed mechanisms, here we test the hypothesis that pairs of self-doped ligand holes spatially condense to provide local spin mome

Ipsita Mandal, Shreya Saha, Rahul Ghosh

We investigate how the signatures of the topological properties of the bandstructures for nodal-point semimetals are embedded in the response coefficients, arising in two distinct experimental set-ups, by taking the Rarita-Schwinger-Weyl (RSW) semimetal as an example. The first scenario involves the computation of third-rank tensors representing second-order

David Raske

It is well known that positive Green's operators are not necessarily positivity preserving. In this paper we investigate the matter of just how far from being positivity preserving a positive Green's operator can be. We will also identify a broad class of Green's operators that are not necessarily positivity preserving but have properties related

F. Kéruzoré, L. E. Bleem, N. Frontiere, N. Krishnan

We introduce picasso, a model designed to predict thermodynamic properties of the intracluster medium based on the properties of halos in gravity-only simulations. The predictions result from the combination of an analytical gas model, mapping gas properties to the gravitational potential, and of a machine learning model to predict the model parameters for i

Yann Guggisberg, Fabian Ziltener

We call a metric space $s$-negligible iff its $s$-dimensional Hausdorff measure vanishes. We show that every countably $m$-rectifiable subset of $\mathbb{R}^{2n}$ can be displaced from every $(2n-m)$-negligible subset by a Hamiltonian diffeomorphism that is arbitrarily $C^\infty$-close to the identity. As a consequence, every countably $n$-rectifiable and $n

Gueter Josmy Faure, Jia-Fong Yeh, Min-Hung Chen, Hung-Ting Su

Long-form video understanding presents unique challenges that extend beyond traditional short-video analysis approaches, particularly in capturing long-range dependencies, processing redundant information efficiently, and extracting high-level semantic concepts. To address these challenges, we propose a novel approach that more accurately reflects human cogn

Christopher Frei, Joachim König, Magdaléna Tinková

We prove analogues of the theorem of Green and Tao on linear constellations in primes, in which the primes under consideration are restricted by certain arithmetic conditions. Our first main result is conditional upon Hooley's Riemann hypothesis and imposes the extra condition that the primes have prescribed primitive roots. Our second main result is unc

Morgan Rogers

A multiplicatively idempotent rig (which we abbreviate to mirig) is a rig satisfying the equation $r^2 = r$. We show that a free mirig on finitely many generators is finite and compute its size. This work was originally motivated by a collaborative effort on the decentralized social network Mastodon to compute the size of the free mirig on two generators.

Nicolas Ledos, Evangelia Ntormousi, Shinsuke Takasao, Kentaro Nagamine

High-redshift ($z\sim2-3$) galaxies accrete circumgalactic gas through cold streams. Recent high-resolution MHD simulations of these streams showed a significant amplification of the intergalactic magnetic field in the shear layer around them. In this work we estimate the magnetisation of high-redshift galaxies that would result purely due to the accretion o

E. D. Emtsova, A. N. Petrov, A. V. Toporensky

We study the energy-momentum characteristics of the rotating black hole - Kerr solution of general relativity in the Teleparallel Equivalent of General Relativity (TEGR) and the Symmetric Teleparallel Equivalent of General Relativity (STEGR). The previously constructed spacetime covariant and Lorentz invariant expressions for conserved Noether currents, supe

Raoyuan Zhao, Abdullatif Köksal, Yihong Liu, Leonie Weissweiler

Traditional benchmarking in NLP typically involves using static held-out test sets. However, this approach often results in an overestimation of performance and lacks the ability to offer comprehensive, interpretable, and dynamic assessments of NLP models. Recently, works like DynaBench (Kiela et al., 2021) and CheckList (Ribeiro et al., 2020) have addressed

Mona Sheikh Zeinoddin, Chiara Lena, Jiongqi Qu, Luca Carlini

Robotic-assisted surgery (RAS) relies on accurate depth estimation for 3D reconstruction and visualization. While foundation models like Depth Anything Models (DAM) show promise, directly applying them to surgery often yields suboptimal results. Fully fine-tuning on limited surgical data can cause overfitting and catastrophic forgetting, compromising model r

Ismail Rasim Ulgen, Shreeram Suresh Chandra, Junchen Lu, Berrak Sisman

Synthesizing the voices of unseen speakers remains a persisting challenge in multi-speaker text-to-speech (TTS). Existing methods model speaker characteristics through speaker conditioning during training, leading to increased model complexity and limiting reproducibility and accessibility. A low-complexity alternative would broaden the reach of speech synth

Jonathan Bourne

The digitisation of historical print media archives is crucial for increasing accessibility to contemporary records. However, the process of Optical Character Recognition (OCR) used to convert physical records to digital text is prone to errors, particularly in the case of newspapers and periodicals due to their complex layouts. This paper introduces Context

Nihar Shah

In many online domains, Sybil networks -- or cases where a single user assumes multiple identities -- is a pervasive feature. This complicates experiments, as off-the-shelf regression estimators at least assume known network topologies (if not fully independent observations) when Sybil network topologies in practice are often unknown. The literature has excl

Peter A. Brooksbank, Martin D. Kassabov, James B. Wilson

This article introduces a class of efficiently computable null patterns for tensor data. The class includes familiar patterns such as block-diagonal decompositions explored in statistics and signal processing, low-rank tensor decompositions, and Tucker decompositions. It also includes a new family of null patterns -- not known to be detectable by current met

Naoki Wake, Atsushi Kanehira, Kazuhiro Sasabuchi, Jun Takamatsu

Video action localization aims to find the timings of specific actions from a long video. Although existing learning-based approaches have been successful, they require annotating videos, which comes with a considerable labor cost. This paper proposes a training-free, open-vocabulary approach based on emerging off-the-shelf vision-language models (VLMs). The

Connor Behan, Rodrigo S. Pitombo

There are holographic superconformal theories in all dimensions between two and six which allow arbitrary tree-level four-point functions to be fixed by basic consistency conditions. Although Mellin space is usually the most efficient setting for imposing these contraints, four-point functions in two dimensions have thus far been an exception due to their mo

Patrik Milán Veres, Anna Franckowiak, Sjoert van Velzen, Bjoern Adebahr

Context. To date, three nuclear transients have been associated with high-energy neutrino events. These transients are generally thought to be powered by tidal disruptions of stars (TDEs) by massive black holes. However, AT2019aalc, hosted in a Seyfert-1 galaxy, was not yet classified due to a lack of multiwavelength observations. Interestingly, the source h

Ya-Chi Chu, Alice Cortinovis

In this work, we analyze the variance of a stochastic estimator for computing Schatten norms of matrices. The estimator extracts information from a single sketch of the matrix, that is, the product of the matrix with a few standard Gaussian random vectors. While this estimator has been proposed and used in the literature before, the existing variance bounds

Axel Brandenburg, Oksana Iarygina, Evangelos I. Sfakianakis, Ramkishor Sharma

We describe a novel proposal for inflationary magnetogenesis by identifying the non-Abelian sector of Spectator Chromo Natural Inflation (SCNI) with the $\rm{SU(2)}_{\rm L}$ sector of the Standard Model. This mechanism relies on the recently discovered attractor of SCNI in the strong backreaction regime, where the gauge fields do not decay on super-horizon s

Mattia Sabatini, Tommaso Bertapelle, Paolo Villoresi, Giuseppe Vallone

Quantum key distribution (QKD) is emerging as a cutting-edge application of quantum technology, gradually integrating into the industrial landscape. Many protocols employing discrete or continuous variables have been developed over time. Whereas the firsts usually excel in covering longer distances, the seconds are typically superior in producing higher secr

Harry Goodhew, Ayngaran Thavanesan, Aron C. Wall

The CPT theorem states that a unitary and Lorentz-invariant theory must also be invariant under a discrete symmetry $\mathbf{CRT}$ which reverses charge, time, and one spatial direction. In this article, we study a $\mathbb{Z}_2 \times \mathbb{Z}_2$ symmetry group, in which two of the nontrivial symmetries (``Reflection Reality'' and a 180 degree rot

Rodrigo A. Fontenele, Natanael C. Costa, Thereza Paiva, Raimundo R. dos Santos

The attractive Hubbard model has become a model readily realizable with ultracold atoms on optical lattices. However, the superconducting (superfluid) critical temperatures, $T_c$'s, are still somewhat smaller than the lowest temperatures achieved in experiments. Here we consider two possible routes, generically called layering, to increase $T_c$: a bila

Antonio Rago, Bence Palfi, Purin Sukpanichnant, Hannibal Nabli

AI-driven tools for healthcare are widely acknowledged as potentially beneficial to health practitioners and patients, e.g. the QCancer regression tool for cancer risk prediction. However, for these tools to be trusted, they need to be supplemented with explanations. We examine how explanations' content and format affect user comprehension and trust when

Valeria Giustarini, Sara Ugolini

We introduce the blockwise gluing construction. This describes residuated integral chains which can be decomposed into (possibly) partial algebras, stacked one on top of the other, and such that elements in a certain component multiply in blocks, i.e., in the same way, with respect to lower components. This construction generalizes that of 1-sums (or ordinal

Andrew Koh, Sivakorn Sanguanmoo

We analyze how uncertain technologies should be robustly regulated and how regulation should evolve with new information. An adaptive sandbox comprising a zero marginal tax up to an evolving quantity limit is (i) robust: it delivers optimal payoff guarantees when the agent's learning process and/or preferences are chosen adversarially; (ii) dominant: it

Zhuoping Zhou, Davoud Ataee Tarzanagh, Bojian Hou, Qi Long

This paper examines the issue of fairness in the estimation of graphical models (GMs), particularly Gaussian, Covariance, and Ising models. These models play a vital role in understanding complex relationships in high-dimensional data. However, standard GMs can result in biased outcomes, especially when the underlying data involves sensitive characteristics

Marco Palla, Laura Magrini, Emanuele Spitoni, Francesca Matteucci

Context. Recent evidences from spectroscopic surveys point towards the presence of a metal-poor, young stellar population in the chemical thin disk. In this context, the investigation of the spatial distribution and time evolution of precise, unbiased abundances is fundamental to disentangle the scenarios of evolution of the Galaxy. Aims. We study the evolut

Ari S. Benjamin, Christian Pehle, Kyle Daruwalla

A natural strategy for continual learning is to weigh a Bayesian ensemble of fixed functions. This suggests that if a (single) neural network could be interpreted as an ensemble, one could design effective algorithms that learn without forgetting. To realize this possibility, we observe that a neural network classifier with N parameters can be interpreted as

J. Aalbers, D. S. Akerib, A. K. Al Musalhi, F. Alder

The broad physics reach of the LUX-ZEPLIN (LZ) experiment covers rare phenomena beyond the direct detection of dark matter. We report precise measurements of the extremely rare decay of $^{124}$Xe through the process of two-neutrino double electron capture (2$ν$2EC), utilizing a $1.39\,\mathrm{kg} \times \mathrm{yr}$ isotopic exposure from the first LZ scien

Aaron Baier-Reinio, Patrick E. Farrell

We derive and analyze a broad class of finite element methods for numerically simulating the stationary, low Reynolds number flow of concentrated mixtures of several distinct chemical species in a common thermodynamic phase. The underlying partial differential equations that we discretize are the Stokes$\unicode{x2013}$Onsager$\unicode{x2013}$Stefan$\unicode

Saikat Banerjee, Stephan Humeniuk, Alan R. Bishop, Avadh Saxena

We extend the concept of conventional multiferroicity \ -- where ferroelectric and ferromagnetic orders coexist \ -- to include multipolar degrees of freedom. Specifically, we explore how this phenomenon emerges in $4d^2/5d^2$ Mott insulators with strong spin-orbit and Hund's couplings. Our study uncovers the origin of magnetic multipolar interactions in

Jack M. Buckingham, Ivo Couckuyt, Juergen Branke

Bayesian optimization is a sample-efficient method for solving expensive, black-box optimization problems. Stochastic programming concerns optimization under uncertainty where, typically, average performance is the quantity of interest. In the first stage of a two-stage problem, here-and-now decisions must be made in the face of uncertainty, while in the sec

Søren Eilers, Sophie Emma Zegers

The quantum lens spaces form a natural and well-studied class of noncommutative spaces which can be subjected to classification using algebraic invariants by drawing on the fully developed classification theory of unital graph $C^*$-algebras. We introduce the problem of deciding when two quantum lens spaces are equivariantly isomorphic, and solve it in certa

Wenxuan Tan, Nicholas Roberts, Tzu-Heng Huang, Jitian Zhao

Parameter-efficient fine-tuning (PEFT) techniques have unlocked the potential to cheaply and easily specialize large pretrained models. However, the most prominent approaches, like low-rank adapters (LoRA), depend on heuristics or rules-of-thumb for their architectural choices -- potentially limiting their performance for new models and architectures. This l

L. Beirão da Veiga, D. Mora, A. Silgado

In this work we design a novel $C^1$-conforming virtual element method of arbitrary order $k \geq 2$, to solve the biharmonic problem on a domain with curved boundary and internal curved interfaces in two dimensions. By introducing a suitable stabilizing form, we develop a rigorous interpolation, stability and convergence analysis obtaining optimal error est

Zihao Sheng, Zilin Huang, Sikai Chen

Model-based reinforcement learning (RL) is anticipated to exhibit higher sample efficiency compared to model-free RL by utilizing a virtual environment model. However, it is challenging to obtain sufficiently accurate representations of the environmental dynamics due to uncertainties in complex systems and environments. An inaccurate environment model may de

Francesco Argenziano, Michele Brienza, Vincenzo Suriani, Daniele Nardi

Task planning for robots in real-life settings presents significant challenges. These challenges stem from three primary issues: the difficulty in identifying grounded sequences of steps to achieve a goal; the lack of a standardized mapping between high-level actions and low-level commands; and the challenge of maintaining low computational overhead given th

Jiayi Zhou, Jiaming Ji, Juntao Dai, Dong Li

Aligning the behavior of Large language models (LLMs) with human intentions and values remains a critical challenge. Reinforcement learning from human feedback (RLHF) aligns LLMs by training a reward model (RM) on human preferences and fine-tuning the LLMs to maximize RM feedback. Despite its effectiveness and popularity, RLHF is prone to biased local optimi

Siddheswar Kundu

The flagged refined stable Grothendieck polynomials of skew shapes generalize several polynomials like stable Grothendieck polynomials, flagged skew Schur polynomials. In this paper, we provide a combinatorial expansion of the flagged refined skew stable Grothendieck polynomial in terms of key polynomials. We present this expansion by imposing a Demazure cry

Jeffrey S. Oishi, Benjamin P. Brown

We present a linear analysis of a minimal model of moist convection under a variety of atmospheric conditions. The stationary solutions that we analyze include both fully saturated and partially unsaturated atmospheres in both unconditionally and conditionally unstable cases. We find that all of the solutions we consider are linearly unstable via exchange of

Albert Gassol Puigjaner, Irvin Aloise, Patrik Schmuck

Visual localization is crucial for Computer Vision and Augmented Reality (AR) applications, where determining the camera or device's position and orientation is essential to accurately interact with the physical environment. Traditional methods rely on detailed 3D maps constructed using Structure from Motion (SfM) or Simultaneous Localization and Mapping

Carlos Alegria, Susanna Caroppo, Giordano Da Lozzo, Marco D'Elia

We study upward pointset embeddings (UPSEs) of planar $st$-graphs. Let $G$ be a planar $st$-graph and let $S \subset \mathbb{R}^2$ be a pointset with $|S|= |V(G)|$. An UPSE of $G$ on $S$ is an upward planar straight-line drawing of $G$ that maps the vertices of $G$ to the points of $S$. We consider both the problem of testing the existence of an UPSE of $G$

Simon Wassing, Stefan Langer, Philipp Bekemeyer

Physics-informed neural networks have gained popularity as a deep-learning based parametric partial differential equation solver. Especially for engineering applications, this approach is promising because a single neural network could substitute many classical simulations in multi-query scenarios. Only recently, researchers have successfully solved subsonic

Giulia Garcia Lorenzana, Ada Altieri, Giulio Biroli, Michel Fruchart

Disordered systems generically exhibit aging and a glass transition. Previous studies have long suggested that non-reciprocity tends to destroy glassiness. Here, we show that this is not always the case using a bipartite spherical Sherrington-Kirpatrick model that describes the antagonistic coupling between two identical complex agents modeled as macroscopic

Jiří Janoš, Petr Slavíček, Basile F. E. Curchod

Over the last decades, theoretical photochemistry has produced multiple techniques to simulate the nonadiabatic dynamics of molecules. Surprisingly, much less effort has been devoted to adequately describing the first step of a photochemical or photophysical process: photoexcitation. Here, we propose a formalism to include the effect of a laser pulse in traj

Andrzej Krasiński

The maximally extended Reissner--Nordström (RN) spacetime with $e^2 < m^2$ can be interpreted either as an infinite chain of asymptotically flat regions connected by tunnels between timelike singularities or as a set of just one pair of asymptotically flat regions and one tunnel; the repetitions of this set in the infinite chain being identified. The second

Yuejiang Liu, Jubayer Ibn Hamid, Annie Xie, Yoonho Lee

Predicting and executing a sequence of actions without intermediate replanning, known as action chunking, is increasingly used in robot learning from human demonstrations. Yet, its effects on the learned policy remain inconsistent: some studies find it crucial for achieving strong results, while others observe decreased performance. In this paper, we first d

Jack Tigar Humphries, Neel Natu, Kostis Kaffes, Stanko Novaković

SmartNICs are increasingly deployed in datacenters to offload tasks from server CPUs, improving the efficiency and flexibility of datacenter security, networking and storage. Optimizing cloud server efficiency in this way is critically important to ensure that virtually all server resources are available to paying customers. Userspace system software, specif

Anton V. Proskurnikov, Francesco Bullo

Recent studies on stability and contractivity have highlighted the importance of semi-inner products, which we refer to as pairings, associated with general norms. A pairing is a binary operation that relates the derivative of a curve&#39;s norm to the radius-vector of the curve and its tangent. This relationship, known as the curve norm derivative formula,

Devashish Tupkary, Shlok Nahar, Pulkit Sinha, Norbert Lütkenhaus

We present a finite-size security proof of the decoy-state BB84 QKD protocol against coherent attacks, using entropic uncertainty relations, for imperfect detectors. We apply this result to the case of detectors with imperfectly characterized basis-efficiency mismatch. Our proof works by obtaining a suitable bound on the phase error rate, without requiring a

Moritz Heinlein, Sankaranarayanan Subramanian, Sergio Lucia

Robust model predictive control algorithms are essential for addressing unavoidable errors due to the uncertainty in predicting real-world systems. However, the formulation of such algorithms typically results in a trade-off between conservatism and computational complexity. Monotone systems facilitate the efficient computation of reachable sets and thus the

Shuyi Ouyang, Jinyang Zhang, Xiangye Lin, Xilai Wang

In clinical practice, segmenting specific lesions based on the needs of physicians can significantly enhance diagnostic accuracy and treatment efficiency. However, conventional lesion segmentation models lack the flexibility to distinguish lesions according to specific requirements. Given the practical advantages of using text as guidance, we propose a novel

Benjamin Clavié

This paper presents rerankers, a Python library which provides an easy-to-use interface to the most commonly used re-ranking approaches. Re-ranking is an integral component of many retrieval pipelines; however, there exist numerous approaches to it, relying on different implementation methods. rerankers unifies these methods into a single user-friendly inter

Ugo Dal Lago, Zeinab Galal, Giulia Giusti

A $λ$-calculus is introduced in which all programs can be evaluated in probabilistic polynomial time and in which there is sufficient structure to represent sequential cryptographic constructions and adversaries for them, even when the latter are oracle-based. A notion of observational equivalence capturing computational indistinguishability and a class of a

Yuji Lin, Junhui Hou, Xianqiang Lyu, Qian Zhao

In this paper, we delve into the realm of 4-D light fields (LFs) to enhance underwater imaging plagued by light absorption, scattering, and other challenges. Contrasting with conventional 2-D RGB imaging, 4-D LF imaging excels in capturing scenes from multiple perspectives, thereby indirectly embedding geometric information. This intrinsic property is antici

Gurvan Richardeau, Samy Chali, Erwan Le Merrer, Camilla Penzo

Large Language Models (LLMs) are nowadays prompted for a wide variety of tasks. In this article, we investigate their ability in reciting and generating graphs. We first study the ability of LLMs to regurgitate well known graphs from the literature (e.g. Karate club or the graph atlas)4. Secondly, we question the generative capabilities of LLMs by asking for

CMS Collaboration

A search for dark matter (DM) particles produced in association with bottom quarks is presented. The analysis uses proton-proton collision data at a center-of-mass energy of $\sqrt{s}$ = 13 TeV, corresponding to an integrated luminosity of 138 fb$^{-1}$. The search is performed in the final state with large missing transverse momentum and a pair of jets orig

Jixin Hou, Zhengwang Wu, Xianyan Chen, Li Wang

The surface morphology of the developing mammalian brain is crucial for understanding brain function and dysfunction. Computational modeling offers valuable insights into the underlying mechanisms for early brain folding. Recent findings indicate significant regional variations in brain tissue growth, while the role of these variations in cortical developmen

Xiang Cao, Xiaoqun Zhang

Diffusion models have been widely studied as effective generative tools for solving inverse problems. The main ideas focus on performing the reverse sampling process conditioned on noisy measurements, using well-established numerical solvers for gradient updates. Although diffusion-based sampling methods can produce high-quality reconstructions, challenges p

Juan Miguel Lopez Alcaraz, Nils Strodthoff

Ensuring timely and accurate diagnosis of medical conditions is paramount for effective patient care. Electrocardiogram (ECG) signals are fundamental for evaluating a patient&#39;s cardiac health and are readily available. Despite this, little attention has been given to the remarkable potential of ECG data in detecting non-cardiac conditions. In our study,

Hongjie Dong, Hyunwoo Kwon

We consider nonstationary Stokes equations in nondivergence form with variable viscosity coefficients and generalized Navier slip boundary conditions with slip tensor $\mathcal{A}$ in a domain $Ω$ in $\mathbb{R}^d$. First, under the assumption that slip matrix $\mathcal{A}$ is sufficiently smooth, we establish a priori local regularity estimates for solution

German Sborlini, Jose Rios-Sanchez

We report recent progress on the development of a local renormalisation formalism based on causal loop-tree duality (cLTD). By performing an expansion around the UV-propagator in an Euclidean space, we manage to build counter-terms to cancel the non-integrable terms in the UV limit. This procedure is then combined with the so-called causal representation, an