April 2024 arXiv papers — page 11

Khac-Hoang Ngo, Giuseppe Durisi, Andrea Munari, Francisco Lázaro

We investigate the age of information (AoI) in a scenario where energy-harvesting devices send status updates to a gateway following the slotted ALOHA protocol and receive no feedback. We let the devices adjust the transmission probabilities based on their current battery level. Using a Markovian analysis, we derive analytically the average AoI. We further p

Calder Miller, Annette N. Carroll, Junyu Lin, Henrik Hirzler

Polar molecules confined in an optical lattice are a versatile platform to explore spin-motion dynamics based on strong, long-range dipolar interactions. The precise tunability of Ising and spin-exchange interactions with both microwave and dc electric fields makes the molecular system particularly suitable for engineering complex many-body dynamics. Here, w

Rajashri Parida, Ashirbad Padhan, Tapan Mishra

We investigate the topological properties of hardcore bosons possessing nearest-neighbor repulsive interactions on a two-leg ladder. We show that by allowing nearest neighbour dimerized interactions instead of hopping dimerization, the system exhibits topological phases and phase transitions under proper conditions. First, by assuming uniform hopping through

Fangcheng Liu, Yehui Tang, Zhenhua Liu, Yunsheng Ni

Speculative decoding has demonstrated its effectiveness in accelerating the inference of large language models while maintaining a consistent sampling distribution. However, the conventional approach of training a separate draft model to achieve a satisfactory token acceptance rate can be costly. Drawing inspiration from early exiting, we propose a novel sel

Ajay C. J., Ben McMillan, Arkaprava Bokshi, Alessandro di Siena

Toroidal Alfv\'en eigenmodes (TAEs) can transport fusion-born energetic particles out of the plasma volume, thereby decreasing plasma self-heating efficiency and possibly damaging reactor walls. Therefore, understanding TAE destabilisation and identifying saturation mechanisms is crucial to achieving burning plasma. While TAEs have been studies extensively i

Laixi Shi, Eric Mazumdar, Yuejie Chi, Adam Wierman

To overcome the sim-to-real gap in reinforcement learning (RL), learned policies must maintain robustness against environmental uncertainties. While robust RL has been widely studied in single-agent regimes, in multi-agent environments, the problem remains understudied -- despite the fact that the problems posed by environmental uncertainties are often exace

Daniel Altman, Anita Liebenau

We prove that suitably generic pairs of linear equations on an even number of variables are uncommon. This verifies a conjecture of Kam\v{c}ev, Morrison and the second author. Moreover, we prove that any large system containing such a $(2\times k)$-system as a minimal subsystem is uncommon.

Reed Hodges

We describe the application of effective field theories for quantum chromodynamics (QCD) to two bound states involving heavy quarks: the $T_{cc}^+$ exotic meson and the $J/\psi$. We study the decay of the $T_{cc}^+$ in an effective theory for hadronic molecules, and find agreement with experiment. We also use the nonrelativistic QCD (NRQCD) factorization for

Manasi Kulkarni, Siddhi Durve, Bochen Jia

As digital technology becomes increasingly embedded in daily life, its impact on social interactions has become a critical area of study, particularly concerning cyberbullying. This meta-analysis investigates the dual role of technology in cyberbullying both as a catalyst that can exacerbate the issue and as a potential solution. Cyberbullying, characterized

Danny Z. Chen, Ziyun Huang, Yangwei Liu, Jinhui Xu

In this paper, we study a generalization of the classical Voronoi diagram, called clustering induced Voronoi diagram (CIVD). Different from the traditional model, CIVD takes as its sites the power set $U$ of an input set $P$ of objects. For each subset $C$ of $P$, CIVD uses an influence function $F(C,q)$ to measure the total (or joint) influence of all objec

Piersilvio De Bartolomeis, Javier Abad, Konstantin Donhauser, Fanny Yang

Randomized trials are considered the gold standard for making informed decisions in medicine, yet they often lack generalizability to the patient populations in clinical practice. Observational studies, on the other hand, cover a broader patient population but are prone to various biases. Thus, before using an observational study for decision-making, it is c

Jakub Gajarský, Rose McCarty

Graph classes of bounded tree rank were introduced recently in the context of the model checking problem for first-order logic of graphs. These graph classes are a common generalization of graph classes of bounded degree and bounded treedepth, and they are a special case of graph classes of bounded expansion. We introduce a notion of decomposition for these

Andisheh Khedri, Pascal Stadler, Kirsten Bark, Matteo Lodi

With the surge of quantum computing platforms that continue to push the boundaries of capabilities of noisy intermediate-scale quantum computers, there is a growing interest in finding relevant applications and quantifying the corresponding error budgets. We present a simulation of nuclear magnetic resonance (NMR) spectroscopy of small organic molecules on p

Brice Huang

We show that the capacity of the Ising perceptron is with high probability upper bounded by the constant $\alpha_\star \approx 0.833$ conjectured by Krauth and M\'ezard, under the condition that an explicit two-variable function $\mathscr{S}_*(\lambda_1,\lambda_2)$ is maximized at $(1,0)$. The earlier work of Ding and Sun proves the matching lower bound subj

Scott Viteri, Max Lamparth, Peter Chatain, Clark Barrett

Chain-of-Thought (CoT) reasoning often fails to faithfully reflect a language model's underlying decision process. We address this by introducing a Markovian language model framework with an autoencoder-style reasoning bottleneck: all information flowing from question to answer must pass through a bounded-length CoT, creating a bandwidth bottleneck analogous

José A. Carrillo, Stefano Fronzoni, Endre Süli

We construct a finite element method for the numerical solution of a fractional porous medium equation on a bounded open Lipschitz polytopal domain $\Omega \subset \mathbb{R}^{d}$, where $d = 2$ or $3$. The pressure in the model is defined as the solution of a fractional Poisson equation, involving the fractional Neumann Laplacian in terms of its spectral de

Shun-Qing Zhang

Integrated correlator of four superconformal stress-tensor primaries in $SU(N)$ $\mathcal{N}=4$ super Yang-Mills (SYM) theory in the perturbative limit takes a remarkably simple form, where the $L$-loop coefficient is given by a rational multiple of $\zeta(2L+1)$. In this letter, we extend the previous analysis of expressing the perturbative integrated corre

Leon Ruf, Elke Scheer, Angelo Di Bernardo

Logic circuits consist of devices that can be controlled between two distinct states. The recent demonstration that a superconducting current flowing in a constriction can be controlled via a gate voltage ($V_G$) - can lead to superconducting logic with better performance than existing logics. However, before such logic is developed, high reproducibility in

Reiko Liu, Wen-Jie Ma

We establish the nonperturbative celestial optical theorem from the unitarity of $S$-matrix. This theorem provides a set of nonperturbative bootstrap equations of the conformal partial wave (CPW) coefficients. The celestial optical theorem implies that the imaginary part of CPW coefficient with appropriate conformal dimensions is non-negative. By making cert

Priyanka Gawade, Anupreeta More, Surhud More, Akisato Kimura

Strong lensing of background galaxies provides important information about the matter distribution around lens galaxies. Traditional modelling of such strong lenses is both time and resource intensive. Fast and automated analysis methods are the need of the hour given large upcoming surveys. In this work, we build and train a simple convolutional neural netw

Xingyuan Zhang, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl

Pretraining and finetuning models has become increasingly popular in decision-making. But there are still serious impediments in Imitation Learning from Observation (ILfO) with pretrained models. This study identifies two primary obstacles: the Embodiment Knowledge Barrier (EKB) and the Demonstration Knowledge Barrier (DKB). The EKB emerges due to the pretra

Chenyang Liu, Keyan Chen, Bowen Chen, Haotian Zhang

Remote Sensing Image Change Captioning (RSICC) aims to describe surface changes between multi-temporal remote sensing images in language, including the changed object categories, locations, and dynamics of changing objects (e.g., added or disappeared). This poses challenges to spatial and temporal modeling of bi-temporal features. Despite previous methods pr

Sihan Chen, Xander M. de Wit, Michel Fruchart, Federico Toschi

Intermittency refers to the broken self-similarity of turbulent flows caused by anomalous spatio-temporal fluctuations. In this Letter, we ask how intermittency is affected by a non-dissipative viscosity, known as odd viscosity (also Hall or gyro-viscosity), which appears in parity-breaking fluids such as magnetized polyatomic gases, electron fluids under ma

Sitan Chen, Vasilis Kontonis, Kulin Shah

We study the problem of learning mixtures of $k$ Gaussians in $d$ dimensions. We make no separation assumptions on the underlying mixture components: we only require that the covariance matrices have bounded condition number and that the means and covariances lie in a ball of bounded radius. We give an algorithm that draws $d^{\mathrm{poly}(k/\varepsilon)}$

Hidetaka Kuniyoshi, Souvik Bose, Takaaki Yokoyama

Magnetic tornadoes, characterized as impulsive Alfven waves initiated by photospheric vortices in intergranular lanes, are considered efficient energy channels to the corona. Despite their acknowledged importance for solar coronal heating, their observational counterparts from the corona have not been well understood. To address this issue, we use a radiativ

Kebin Wu, Wenbin Li, Xiaofei Xiao

The scarcity of labeled data in real-world scenarios is a critical bottleneck of deep learning's effectiveness. Semi-supervised semantic segmentation has been a typical solution to achieve a desirable tradeoff between annotation cost and segmentation performance. However, previous approaches, whether based on consistency regularization or self-training, tend

Yuguang Yao, Steven Grosz, Sijia Liu, Anil Jain

The recent progress in generative models has revolutionized the synthesis of highly realistic images, including face images. This technological development has undoubtedly helped face recognition, such as training data augmentation for higher recognition accuracy and data privacy. However, it has also introduced novel challenges concerning the responsible us

Mihai I. Florea, Yurii Nesterov

First order methods endowed with global convergence guarantees operate using global lower bounds on the objective. The tightening of the bounds has been shown to increase both the theoretical guarantees and the practical performance. In this work, we define a global lower bound for smooth differentiable objectives that is optimal with respect to the collecte

Shimon Garti

We prove a positive polarized cube relation for infinite cardinals.

Daniel Blackwell, David Clark

Since the advent of AFL, the use of mutational, feedback directed, grey-box fuzzers has become critical in the automated detection of security vulnerabilities. A great deal of research currently goes into their optimisation, including improving the rate at which they achieve branch coverage early in a campaign. We produce an augmented version of LibAFL's `fu

Yiyuan Yang, Ming Jin, Haomin Wen, Chaoli Zhang

Diffusion models have been widely used in time series and spatio-temporal data, enhancing generative, inferential, and downstream capabilities. These models are applied across diverse fields such as healthcare, recommendation, climate, energy, audio, and traffic. By separating applications for time series and spatio-temporal data, we offer a structured persp

Mikahil Lomonosov, Vladimir Shiltsev

Mikhail Lomonosov (1711-1765) is the eminent Russian polymath and a towering figure of the European Enlightenment. This English translation of his seminal work Discourse on Greater Accuracy of Navigation concludes the series of English translations of nine most significant scientific treatises, all of which Lomonosov himself compiled in the volume titled Lom

Luis Renato Gonçalves Dias, Giovanny Snaider Barrera Ramos

We prove a version of the Thom Isotopy Theorem for nonproper semialgebraic maps $f\colon X\rightarrow \mathbb{R}^m$, where $X \subset\mathbb{R}^n$ is a semialgebraic set and $f$ is the restriction to $X$ of a smooth semialgebraic map $F:\mathbb{R}^n\to \mathbb{R}^m$.

Wei-Hong Liang, Raquel Molina, Eulogio Oset

We have studied the $\Omega_c \to \pi^+ (\pi^0, \eta) \pi \Xi^*$ decays, where the final $\pi \Xi^*$ comes from the decay of two resonances around the nominal $\Xi(1820)$, which are generated from the interaction of coupled channels made of a pseudoscalar and a baryon of the decuplet. The $\pi \Xi^*$ mass distributions obtained in the six different reactions

Hong Jin Kang, Fabrice Harel-Canada, Muhammad Ali Gulzar, Violet Peng

Data augmentation techniques apply transformations to existing texts to generate additional data. The transformations may produce low-quality texts, where the meaning of the text is changed and the text may even be mangled beyond human comprehension. Analyzing the synthetically generated texts and their corresponding labels is slow and demanding. To winnow o

Aman Saini, Artem Chernodub, Vipul Raheja, Vivek Kulkarni

We introduce Spivavtor, a dataset, and instruction-tuned models for text editing focused on the Ukrainian language. Spivavtor is the Ukrainian-focused adaptation of the English-only CoEdIT model. Similar to CoEdIT, Spivavtor performs text editing tasks by following instructions in Ukrainian. This paper describes the details of the Spivavtor-Instruct dataset

Sena Aydin, Seyedeh Maryam Salehi, Kai Töpfer, Markus Meuwly

The dynamics of lysozyme is probed by attaching -SCN to all alanine-residues. The 1-dimensional infrared spectra exhibit frequency shifts in the position of the maximum absorption by 4 cm$^{-1}$ which is consistent with experiments in different solvents and indicates moderately strong interactions of the vibrational probe with its environment. Isotopic subst

Daniel Marti-Dafcik, Hugh G. A. Burton, David P. Tew

The performance of quantum algorithms for eigenvalue problems, such as computing Hamiltonian spectra, depends strongly on the overlap of the initial wavefunction and the target eigenvector. In a basis of Slater determinants, the representation of energy eigenstates of systems with $N$ strongly correlated electrons requires a number of determinants that scale

JingChun Wang, Juan Carlos San Vicente Veliz, Markus Meuwly

The atom-exchange and atomization dissociation dynamics for the N($^4$S) + N$_2(^1 \Sigma_{\rm g}^+)$ reaction is studied using a reproducing kernel Hilbert space (RKHS)-based, global potential energy surface (PES) at the MRCI-F12/aug-cc-pVTZ-F12 level of theory. For the atom exchange reaction $({\rm N_A N_B} + {\rm N_C} \rightarrow {\rm N_A N_C} + {\rm N_B}

Ozkan Cigdem, Shengjia Chen, Chaojie Zhang, Kyunghyun Cho

A survival analysis model for predicting time-to-total knee replacement (TKR) was developed using features from medical images and clinical measurements. Supervised and self-supervised deep learning approaches were utilized to extract features from radiographs and magnetic resonance images. Extracted features were combined with clinical and image assessments

Cristiano B. de Oliveira, Joao C. Neves, Rafael O. Ribeiro, David Menotti

The Pal\'acio do Planalto, office of the President of Brazil, was invaded by protesters on January 8, 2023. Surveillance videos taken from inside the building were subsequently released by the Brazilian Supreme Court for public scrutiny. We used segments of such footage to create the UFPR-Planalto801 dataset for people tracking and re-identification in a rea

Alice Bonino, Patricia Schmidt, Geraint Pratten

Orbital eccentricity in compact binaries is considered to be a key tracer of their astrophysical origin, and can be inferred from gravitational-wave observations due to its imprint on the emitted signal. For a robust measurement, accurate waveform models are needed. However, ambiguities in the definition of eccentricity can obfuscate the physical meaning and

Pablo Barenbaum, Delia Kesner, Mariana Milicich

This work provides the first inductive definition of useful CBV evaluation. For that, we first restrict the substitution operation in the Value Substitution Calculus to be linear, yielding the LCBV strategy. We then further restrict substitution in LCBV, so that substitution contributes to the progress of the computation. This optimisation is the UCBV strate

Marc Plasseraud, Krishnan Mahesh

A novel method is proposed to identify vortex boundary and center of rotation based on tubular surfaces of constant stagnation pressure and minimum of the stagnation pressure gradient. The method is derived from Crocco's theorem, which ensures that the gradient of stagnation pressure is orthogonal to both the velocity and vorticity vectors. The method is Gal

Guillaume Astruc, Nicolas Dufour, Ioannis Siglidis, Constantin Aronssohn

Determining the location of an image anywhere on Earth is a complex visual task, which makes it particularly relevant for evaluating computer vision algorithms. Yet, the absence of standard, large-scale, open-access datasets with reliably localizable images has limited its potential. To address this issue, we introduce OpenStreetView-5M, a large-scale, open-

Yoshitaka Hatta, Jakob Schoenleber

We present a QCD analysis of the twist-three parton distribution functions associated with the spin-orbit correlation of quarks and gluons in spin-$\frac{1}{2}$ and spin-0 hadrons. We derive exact non-perturbative identities decomposing the spin-orbit correlations into the Wandzura-Wilczek part and the genuine twist-three part. In the spin-$\frac{1}{2}$ case

Dénes Berta, Barna Mendei, Péter Dusán Ispánovity

The plastic deformation of crystalline and other heterogeneous materials often manifests in stochastic intermittent events indicating the criticality of plastic behavior. Previous studies demonstrated that the presence of short-ranged quenched disorder modifies this behavior disrupting long-range static and dynamic correlations consequently localizing disloc

Aaron J. Li, Satyapriya Krishna, Himabindu Lakkaraju

The trustworthiness of Large Language Models (LLMs) refers to the extent to which their outputs are reliable, safe, and ethically aligned, and it has become a crucial consideration alongside their cognitive performance. In practice, Reinforcement Learning From Human Feedback (RLHF) has been widely used to align LLMs with labeled human preferences, but its as

Khashayar Gatmiry, Jonathan Kelner, Holden Lee

We give a new algorithm for learning mixtures of $k$ Gaussians (with identity covariance in $\mathbb{R}^n$) to TV error $\varepsilon$, with quasi-polynomial ($O(n^{\text{poly\,log}\left(\frac{n+k}{\varepsilon}\right)})$) time and sample complexity, under a minimum weight assumption. Our results extend to continuous mixtures of Gaussians where the mixing dist

ATLAS Collaboration

A search for heavy pseudo-scalar ($A$) and scalar ($H$) Higgs bosons decaying into a top-quark pair ($t\bar{t}$) has been performed with 140 fb$^{-1}$ of proton-proton collision data collected by the ATLAS experiment at the Large Hadron Collider at a centre-of-mass energy of $\sqrt{s}=13$ TeV. Interference effects between the signal process and Standard Mode

Sai Krishna K. Hari, Anatoly Zlotnik, Shriram Srinivasan, Kaarthik Sundar

We examine the modeling, simulation, and optimization of district heating systems, which are widely used for thermal transport using steam or hot water as a carrier. We propose a generalizable framework to specify network models and scenario parameters, and develop an optimization method for evaluating system states including pressures, fluid flow rates, and

Sameera Vemulapalli

Orders in number fields provide natural examples of lattices. We ask: what can the successive minima of lattices arising from orders in number fields be? Given an order $\mathcal{O}$ of absolute discriminant $\Delta$ in a degree $n$ number field, let $1=\lambda_0,\dots,\lambda_{n-1}$ denote the successive minima. For $3 \leq n \leq 5$ and many groups $G \sub

Li Qiwei, Francesca Lameiro, Shefali Patel, Cristi-Isaula-Reyes

Non-consensual Intimate Media (NCIM) refers to the distribution of sexual or intimate content without consent. NCIM is common and causes significant emotional, financial, and reputational harm. We developed Hands-Off, an interaction technique for messaging applications that deters non-consensual screenshots. Hands-Off requires recipients to perform a hand ge

Hung Tran

Let $(M, g, \omega, f, \lambda)$ be a K\"{a}hler gradient Ricci soliton in real dimension four. One first observes that it is an integrable Hamiltonian system in a classical sense. Indeed, all known complete examples are toric and the symmetry is intrinsically related to the potential function $f$ and the scalar curvature $\SS$. While another article address

Stefan F. Schouten, Peter Bloem, Ilia Markov, Piek Vossen

Recent work has demonstrated that the latent spaces of large language models (LLMs) contain directions predictive of the truth of sentences. Multiple methods recover such directions and build probes that are described as uncovering a model's "knowledge" or "beliefs". We investigate this phenomenon, looking closely at the impact of context on the probes. Our

Daniel Nichols, Pranav Polasam, Harshitha Menon, Aniruddha Marathe

Optimizing scientific software is a difficult task because codebases are often large and complex, and performance can depend upon several factors including the algorithm, its implementation, and hardware among others. Causes of poor performance can originate from disparate sources and be difficult to diagnose. Recent years have seen a multitude of work that

Jasper Hoffmann, Diego Fernandez, Julien Brosseit, Julian Bernhard

Model predictive control (MPC) is a powerful, optimization-based approach for controlling dynamical systems. However, the computational complexity of online optimization can be problematic on embedded devices. Especially, when we need to guarantee fixed control frequencies. Thus, previous work proposed to reduce the computational burden using imitation learn

Anna Calissano, Matteo Fontana, Gianluca Zeni, Simone Vantini

The analysis of data such as graphs has been gaining increasing attention in the past years. This is justified by the numerous applications in which they appear. Several methods are present to predict graphs, but much fewer to quantify the uncertainty of the prediction. The present work proposes an uncertainty quantification methodology for graphs, based on

Rui Xu, Shu Yang, Yihui Wang, Yu Cai

Mamba, a recent selective structured state space model, excels in long sequence modeling, which is vital in the large model era. Long sequence modeling poses significant challenges, including capturing long-range dependencies within the data and handling the computational demands caused by their extensive length. Mamba addresses these challenges by overcomin

Max Horn, Alice Niemeyer, Cheryl Praeger, Daniel Rademacher

We introduce a new constructive recognition algorithm for finite special linear groups in their natural representation. Given a group $G$ generated by a set of $d\times d$ matrices over a finite field $\mathbb{F}_q$, known to be isomorphic to the special linear group $\mathrm{SL}(d,q)$, the algorithm computes a special generating set $S$ for $G$. These gener

András P. Juhász

Generalized Pl\"ucker numbers are defined to count certain types of tangent lines of generic degree $d$ complex projective hypersurfaces. They can be computed by identifying them as coefficients of GL(2)-equivariant cohomology classes of certain invariant subspaces, the so-called coincident root strata, of the vector space of homogeneous degree $d$ complex p

Igor Rozhansky, Vladimir Fal'ko

In this study, we present an effective tight-binding model for an accurate description of the lowest energy quadruplet of conduction band in a ferromagnetic CrX$_3$ monolayer, tuned to the complementary \textit{ab initio} density functional theory simulations. This model, based on a minimum number of chromium orbitals, captures a distinctively flat dispersio

Ning Ning

Markov random fields (MRFs) are invaluable tools across diverse fields, and spatiotemporal MRFs (STMRFs) amplify their effectiveness by integrating spatial and temporal dimensions. However, modeling spatiotemporal data introduces additional hurdles, including dynamic spatial dimensions and partial observations, prevalent in scenarios like disease spread anal

Jens Hoppe

Several ways to reduce to a first order ODE the non-linear PDE's governing the relativistic motion of an axially symmetric membrane in 4 space time dimensions, as well as examples for a previously found non-trivial transformation generating solutions, are given.

Min Woong Ahn

In his 1994 work, Shallit introduced a rule for determining leap years that generalizes both the historically used Julian calendar and the contemporary Gregorian calendar. This rule depends on a so-called intercalation sequence. According to what we term Shallit's law of leap years, almost every point of the interval $[0,1]$ with respect to Lebesgue measure

Wan-Chi Hsin, Uri T. Eden, Emily P. Stephen

Functional brain networks can change rapidly as a function of stimuli or cognitive shifts. Tracking dynamic functional connectivity is particularly challenging as it requires estimating the structure of the network at each moment as well as how it is shifting through time. In this paper, we describe a general modeling framework and a set of specific models t

Min Woong Ahn

The continued fraction mapping maps a number in the interval $[0,1)$ to the sequence of its partial quotients. When restricted to the set of irrationals, which is a subspace of the Euclidean space $\mathbb{R}$, the continued fraction mapping is a homeomorphism onto the product space $\mathbb{N}^{\mathbb{N}}$, where $\mathbb{N}$ is a discrete space. In this s

Aidan Z. H. Yang, Yoshiki Takashima, Brandon Paulsen, Josiah Dodds

Rust is a programming language that combines memory safety and low-level control, providing C-like performance while guaranteeing the absence of undefined behaviors by default. Rust's growing popularity has prompted research on safe and correct transpiling of existing code-bases to Rust. Existing work falls into two categories: rule-based and large language

Mario Corrales-Astorgano, David Escudero-Mancebo, Lourdes Aguilar, Valle Flores-Lucas

Rubrics are a commonly used tool for labeling voice corpora in speech quality assessment, although their application in the context of pathological speech remains relatively limited. In this study, we introduce a comprehensive rubric based on various dimensions of speech quality, including phonetics, fluency, and prosody. The objective is to establish standa

Václav Pavlíček, Ayush Bhandari

Sampling theory in fractional Fourier Transform (FrFT) domain has been studied extensively in the last decades. This interest stems from the ability of the FrFT to generalize the traditional Fourier Transform, broadening the traditional concept of bandwidth and accommodating a wider range of functions that may not be bandlimited in the Fourier sense. Beyond

Andrea Failla, Giulio Rossetti

Pollution of online social spaces caused by rampaging d/misinformation is a growing societal concern. However, recent decisions to reduce access to social media APIs are causing a shortage of publicly available, recent, social media data, thus hindering the advancement of computational social science as a whole. We present a large, high-coverage dataset of s

Heitor R. Medeiros, David Latortue, Eric Granger, Marco Pedersoli

In real-world scenarios, using multiple modalities like visible (RGB) and infrared (IR) can greatly improve the performance of a predictive task such as object detection (OD). Multimodal learning is a common way to leverage these modalities, where multiple modality-specific encoders and a fusion module are used to improve performance. In this paper, we tackl

Yuxuan Yan, Qianqian Yang, Shunpu Tang, Zhiguo Shi

Despite their exceptional performance on various tasks after fine-tuning, pre-trained language models (PLMs) face significant challenges due to growing privacy concerns with data in centralized training methods. We consider federated learning (FL) to fine-tune PLMs in this paper. However, the substantial number of parameters in PLMs poses significant difficu

Victor Gonzalez Avella, Jakub Czartowski, Dardo Goyeneche, Karol Życzkowski

Tomographic reconstruction of quantum states plays a fundamental role in benchmarking quantum systems and accessing information encoded in quantum-mechanical systems. Among the informationally complete sets of quantum measurements, the tight ones provide a linear reconstruction formula and minimize the propagation of statistical errors. However, implementing

Daniel Volya, Prabhat Mishra

We present a benchmarking protocol for universal quantum computers, achieved through the simulation of random dynamical quantum maps. This protocol provides a holistic assessment of system-wide error rates, encapsulating both gate inaccuracies and the errors associated with mid-circuit qubit measurements and resets. By employing random quantum circuits and s

Adria Gomez-Valent, Joan Sola Peracaula

The idea of composite dark energy (DE) is quite natural since on general grounds we expect that the vacuum energy density (associated with the cosmological term $\Lambda$) may appear in combination with other effective forms of DE, denoted X. Here we deal with model $w$XCDM, a simplified version of the old $\Lambda$XCDM model (Grande et al., 2006), and explo

Christopher J Keylock

The turbulent boundary-layer is a region where both preferential dissipation of energy and the production of significant vorticity arises as a consequence of the strong velocity gradients. Previous work has shown that, following a Reynolds decomposition of the enstrophy production, the purely fluctuating contribution is the dominant term and that near the wa

A. Mironov, A. Oreshina, A. Popolitov

Recently, we performed a two $\beta$-ensemble realization of the series of $\beta$-deformed WLZZ matrix models involving $\beta$-deformed Harish-Chandra-Itzykson-Zuber integrals. The realization was derived and studied by using Ward identities, which do not allow one to fix integration contours, these latter were chosen to be real axis for one $\beta$-ensemb

Guanchong Cheng, Lei Ni, Zehao Tang, Yajie Chen

Magnetic reconnection plays a crucial role in the energy release process for different kinds of solar eruptions and activities. The rapid solar eruption requires a fast reconnection model. Plasmoid instability in the reconnecting current sheets is one of the most acceptable fast reconnection mechanisms for explaining the explosive events in the magnetohydrod

Kathryn Knight, Ioana Danciu, Olga Ovchinnikova, Jacob Hinkle

The compilation and analysis of radiological images poses numerous challenges for researchers. The sheer volume of data as well as the computational needs of algorithms capable of operating on images are extensive. Additionally, the assembly of these images alone is difficult, as these exams may differ widely in terms of clinical context, structured annotati

Nicola Rares Franco, Andrea Manzoni, Paolo Zunino, Jan S. Hesthaven

We develop a novel deep learning technique, termed Deep Orthogonal Decomposition (DOD), for dimensionality reduction and reduced order modeling of parameter dependent partial differential equations. The approach consists in the construction of a deep neural network model that approximates the solution manifold through a continuously adaptive local basis. In

Daniel Volya, Andrey Nikitin, Prabhat Mishra

Constrained optimization plays a crucial role in the fields of quantum physics and quantum information science and becomes especially challenging for high-dimensional complex structure problems. One specific issue is that of quantum process tomography, in which the goal is to retrieve the underlying quantum process based on a given set of measurement data. I

Christian Engwer, Mario Ohlberger, Lukas Renelt

This contribution extends the localized training approach, traditionally employed for multiscale problems and parameterized partial differential equations (PDEs) featuring locally heterogeneous coefficients, to the class of linear, positive symmetric operators, known as Friedrichs' operators. Considering a local subdomain with corresponding oversampling doma

Jingyong Ying, Yaqi Xie, Jiao Li, Hongqiao Wang

Deep learning method is of great importance in solving partial differential equations. In this paper, inspired by the failure-informed idea proposed by Gao et.al. (SIAM Journal on Scientific Computing 45(4)(2023)) and as an improvement, a new accurate adaptive deep learning method is proposed for solving elliptic problems, including the interface problems an

Wim Beenakker, Christoph Borschensky, Michael Krämer, Anna Kulesza

We report on updated precision predictions for total cross sections of coloured supersymmetric particle production at the LHC with a centre-of-mass energy of $\sqrt S$ = 13.6 TeV, computed with the modern PDF4LHC21 set. The cross sections are calculated at an approximated NNLO accuracy in QCD and contain corrections from the threshold resummation of soft-glu

Gleiciane S. Aragão, José M. Arrieta, Simone M. Bruschi

In this paper we obtain the continuity of attractors for nonlinear parabolic equations with nonlinear boundary conditions when the boundary of the domain varies very rapidly as a parameter $\epsilon$ goes to zero. We consider the case where the boundary of the domain presents a highly oscillatory behavior as the parameter $\epsilon$ goes to zero. For the cas

Takuya Saito

Discriminantal arrangements are hyperplane arrangements, which are generalized braid ones. They are constructed from given hyperplane arrangements, but their combinatorics are not invariant under combinatorial equivalence. However, it is known that the combinatorics of the discriminantal arrangement are constant on a Zariski open set of the space of hyperpla

Jonas Bresch, Viktor Stein

Regularized optimal transport (OT) has received much attention in recent years starting from Cuturi's introduction of Kullback-Leibler (KL) divergence regularized OT. In this paper, we propose regularizing the OT problem using the family of $\alpha$-R\'enyi divergences for $\alpha \in (0, 1)$. R\'enyi divergences are neither $f$-divergences nor Bregman dista

Lorenzo Betti, Luca Gallo, Johannes Wachs, Federico Battiston

From science to industry, teamwork plays a crucial role in knowledge production and innovation. Most studies consider teams as static groups of individuals, thereby failing to capture how the micro-dynamics of collaborative processes and organizational changes determine team success. Here, we leverage fine-grained temporal data on software development teams

Petter Mæhlum, David Samuel, Rebecka Maria Norman, Elma Jelin

Sentiment analysis is an important tool for aggregating patient voices, in order to provide targeted improvements in healthcare services. A prerequisite for this is the availability of in-domain data annotated for sentiment. This article documents an effort to add sentiment annotations to free-text comments in patient surveys collected by the Norwegian Insti

Hong Nguyen, Hoang Nguyen, Melinda Chang, Hieu Pham

Understanding the severity of conditions shown in images in medical diagnosis is crucial, serving as a key guide for clinical assessment, treatment, as well as evaluating longitudinal progression. This paper proposes Con- PrO: a novel representation learning method for severity assessment in medical images using Contrastive learningintegrated Preference Opti

Alessandro Contu

In 2021, Kashiwara-Kim-Oh-Park constructed cluster algebra structures on the Grothendieck rings of certain monoidal subcategories of the category of finite-dimensional representations of a quantum loop algebra, generalizing Hernandez-Leclerc's pioneering work from 2010. They stated the problem of finding explicit quivers for the seeds they used. We provide a

Liang Zheng, Lian Liu, Zi-Wei Lin, Qi-Ye Shou

In this work, we investigate the collective flow development in high energy proton proton (pp) collisions with a multiphase transport model (AMPT) based on PYTHIA8 initial conditions with a sub-nucleon structure. It is found that the PYTHIA8 based AMPT model can reasonably describe both the charged hadron productions and elliptic flow experimental data measu

Pascal Stadler, Matteo Lodi, Andisheh Khedri, Rolando Reiner

Algorithmic cooling can be used to find correlated states of many-body quantum systems. It is based on quantum circuits that perform nonunitary operations, whose implementation can be challenging on near-term quantum computers. In this work we develop a method that uses inherent qubit noise to implement nonunitary operations and algorithmic cooling. In our a

Nathan Dupont, Amit Vashisht, Nathan Goldman

Fragmentation of an interacting Bose gas refers to the macroscopic occupation of a finite set of single-particle eigenstates. This phenomenon is related to the notion of particle-number squeezing in quantum optics, an exquisite property of quantum states that can offer metrological gain. So far, fragmentation has only been partially achieved in experiments i

Qi Zhang, Lance M. Kaplan, Audun Jøsang, Dong Hyun. Jeong

Competitive Influence Maximization (CIM) involves entities competing to maximize influence in online social networks (OSNs). Current Deep Reinforcement Learning (DRL) methods in CIM rely on simplistic binary opinion models (i.e., an opinion is represented by either 0 or 1) and often overlook the complexity of users' behavioral characteristics and their prior

Nicholas S. Kersting, Yi Li, Aman Mohanty, Oyindamola Obisesan

We introduce Harmonic Robustness, a powerful and intuitive method to test the robustness of any machine-learning model either during training or in black-box real-time inference monitoring without ground-truth labels. It is based on functional deviation from the harmonic mean value property, indicating instability and lack of explainability. We show implemen

Ruijie Xu, Zengzhi Wang, Run-Ze Fan, Pengfei Liu

Amid the expanding use of pre-training data, the phenomenon of benchmark dataset leakage has become increasingly prominent, exacerbated by opaque training processes and the often undisclosed inclusion of supervised data in contemporary Large Language Models (LLMs). This issue skews benchmark effectiveness and fosters potentially unfair comparisons, impeding

Eric Ziebell

The spatially dependent wave speed of a stochastic wave equation driven by space-time white noise is estimated using the local observation scheme. Given a fixed time horizon, we prove asymptotic normality for an augmented maximum likelihood estimator as the resolution level of the observations tends to zero. We show that the expectation and variance of the o

Anas Abdelhakmi, Andrew Lim

The Black-Litterman model is a framework for incorporating forward-looking expert views in a portfolio optimization problem. Existing work focuses almost exclusively on single-period problems with the forecast horizon matching that of the investor. We consider a generalization where the investor trades dynamically and views can be over horizons that differ f